-------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_1.log
  log type:  text
 opened on:  10 May 2023, 18:46:24

. 
. ******************************************************************** 
. ********     TABLE 1:  IVQR BY DECADE                        ********
. ******************************************************************** 
. 
. 
. 
. 
. *** Decade 1 ('80s) *** 
. 
. 
. use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
.         
. 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 keep if year == "_1983" 
(456 observations deleted)

. 
. * Model 1 *
. 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_198
> 3", robust       

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =    1020.96
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8506
                                                  Root MSE        =     .53989

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.389311   .0434806    31.95   0.000      1.30409    1.474531
       _cons |   5.805082   .2831024    20.51   0.000     5.250211    6.359952
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_198
> 3",  quantile(10 25 50 75 90) vce(robust)    

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(5)  = 1739.51
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.442659    .156719     9.21   0.000     1.135495    1.749822
       _cons |    4.79735    1.06047     4.52   0.000     2.718866    6.875834
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.445665    .046358    31.18   0.000     1.354805    1.536525
       _cons |   5.128123   .3298449    15.55   0.000     4.481639    5.774607
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.382874   .0361228    38.28   0.000     1.312074    1.453673
       _cons |   5.839194   .2497026    23.38   0.000     5.349786    6.328602
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.390384   .0859585    16.18   0.000     1.221909     1.55886
       _cons |    6.16554   .5414814    11.39   0.000     5.104256    7.226824
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.297567   .1200135    10.81   0.000     1.062345    1.532789
       _cons |   6.944414   .7657476     9.07   0.000     5.443576    8.445252
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           38.283                 2.172
Constant effect |            0.930                 2.038
Dominance       |            0.000                 2.274
Exogeneity      |            2.475                 2.535
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90)  

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           38.283                 2.045
Constant effect |            0.930                 1.826
Dominance       |            0.000                 2.136
Exogeneity      |            2.475                 2.086
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
. 
. * Model 2 *
. 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_1983", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    1979.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9183
                                                  Root MSE        =     .39931

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.088868   .1029366    10.58   0.000     .8871158     1.29062
       l_pop |    .327336    .075408     4.34   0.000     .1795391    .4751329
       _cons |    3.59226   .4017246     8.94   0.000     2.804894    4.379626
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                   
.                  ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop if year =
> = "_1983",  quantile(10 25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(10) = 4007.08
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.041859   .0774187    13.46   0.000     .8901212    1.193597
       l_pop |   .3974952   .0570279     6.97   0.000     .2857226    .5092678
       _cons |   2.591993   .3198076     8.10   0.000     1.965182    3.218805
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.117049   .1044401    10.70   0.000     .9123503    1.321748
       l_pop |    .297534   .0796202     3.74   0.000     .1414812    .4535868
       _cons |   3.445588   .4243145     8.12   0.000     2.613947    4.277229
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.157011    .157898     7.33   0.000     .8475369    1.466486
       l_pop |   .2383317   .1306448     1.82   0.068    -.0177274    .4943908
       _cons |   4.304599   .7494975     5.74   0.000     2.835611    5.773587
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.050261   .2935274     3.58   0.000     .4749575    1.625564
       l_pop |   .3103741   .2305566     1.35   0.178    -.1415085    .7622567
       _cons |   4.389419   1.141238     3.85   0.000     2.152632    6.626205
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .9781532   .3672186     2.66   0.008     .2584179    1.697888
       l_pop |    .316679   .2252073     1.41   0.160    -.1247192    .7580771
       _cons |   4.872056   .6783817     7.18   0.000     3.542452    6.201659
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                  
.                  estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            9.416                 2.536
Constant effect |            0.888                 2.292
Dominance       |            0.000                 2.350
Exogeneity      |            1.927                 2.558
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                  estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            9.416                 2.167
Constant effect |            0.888                 2.105
Dominance       |            0.000                 2.106
Exogeneity      |            1.927                 2.078
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                  
. 
. 
. * Model 3 *
. 
.                 ivregress liml l_vmt l_pop elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 div8 div9 (l_ln = l_rail1898 l_h
> wy1947) if year == "_1983", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    3105.38
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9288
                                                  Root MSE        =     .37266

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.183185   .1139465    10.38   0.000     .9598537    1.406516
           l_pop |   .2295255   .0940476     2.44   0.015     .0451956    .4138555
elevat_range_msa |  -.0675598   .0677415    -1.00   0.319    -.2003306     .065211
  ruggedness_msa |    7.87936    4.10107     1.92   0.055    -.1585899    15.91731
      heating_dd |  -.0170092   .0056411    -3.02   0.003    -.0280657   -.0059528
      cooling_dd |  -.0273919   .0121506    -2.25   0.024    -.0512067   -.0035772
          sprawl |   .0012222   .0040156     0.30   0.761    -.0066483    .0090927
            div2 |  -.2592773   .2079813    -1.25   0.213    -.6669132    .1483585
            div3 |  -.0742992   .2310816    -0.32   0.748    -.5272109    .3786125
            div4 |  -.1661479   .2483188    -0.67   0.503    -.6528439     .320548
            div5 |  -.2057264   .2353327    -0.87   0.382      -.66697    .2555171
            div6 |  -.3651358    .243682    -1.50   0.134    -.8427437     .112472
            div7 |  -.1400862    .254774    -0.55   0.582    -.6394341    .3592616
            div8 |  -.5093132   .2986151    -1.71   0.088    -1.094588    .0759616
            div9 |  -.4096747   .2875629    -1.42   0.154    -.9732877    .1539383
           _cons |   5.533433   1.023028     5.41   0.000     3.528334    7.538531
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
            div2 div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 di
> v8 div9 if year == "_1983",  quantile(10 25 50 75 90) vce(robust)         

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(75) = 6511.08
                                                       Prob > chi2   =  0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.539308   .1836838     8.38   0.000     1.179295    1.899322
           l_pop |   .0486412   .1390178     0.35   0.726    -.2238287    .3211111
elevat_range_msa |  -.1596016   .0755471    -2.11   0.035    -.3076712    -.011532
  ruggedness_msa |    12.8726   4.169803     3.09   0.002     4.699933    21.04526
      heating_dd |  -.0155114   .0076502    -2.03   0.043    -.0305056   -.0005172
      cooling_dd |  -.0259728   .0167874    -1.55   0.122    -.0588754    .0069298
          sprawl |   .0087599   .0043727     2.00   0.045     .0001896    .0173302
            div2 |   .6842845   1.234689     0.55   0.579    -1.735662    3.104231
            div3 |   .9505459   1.231427     0.77   0.440    -1.463007    3.364099
            div4 |   .7973007   1.187823     0.67   0.502    -1.530789    3.125391
            div5 |   .7170625    1.29643     0.55   0.580    -1.823894    3.258019
            div6 |   .7252184   1.279574     0.57   0.571    -1.782701    3.233137
            div7 |    .737551   1.266626     0.58   0.560     -1.74499    3.220092
            div8 |   .1327679   1.176771     0.11   0.910    -2.173661    2.439197
            div9 |   .7425985   1.331583     0.56   0.577    -1.867257    3.352454
           _cons |   3.968544   1.729434     2.29   0.022     .5789158    7.358173
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.311908   .1265637    10.37   0.000     1.063847    1.559968
           l_pop |   .0892129    .104561     0.85   0.394     -.115723    .2941487
elevat_range_msa |  -.0409581   .0681578    -0.60   0.548     -.174545    .0926288
  ruggedness_msa |   8.402064   4.149643     2.02   0.043     .2689126    16.53522
      heating_dd |   -.023592    .005747    -4.11   0.000    -.0348559   -.0123281
      cooling_dd |  -.0413222   .0140681    -2.94   0.003    -.0688952   -.0137492
          sprawl |   .0034306   .0044651     0.77   0.442    -.0053208    .0121819
            div2 |  -.1878849   .2488829    -0.75   0.450    -.6756865    .2999166
            div3 |   .0356994   .2491285     0.14   0.886    -.4525834    .5239822
            div4 |  -.0366538   .2573437    -0.14   0.887    -.5410382    .4677305
            div5 |  -.1579184   .2762943    -0.57   0.568    -.6994452    .3836085
            div6 |  -.2489494   .2749178    -0.91   0.365    -.7877784    .2898797
            div7 |  -.0240585   .2819405    -0.09   0.932    -.5766517    .5285348
            div8 |  -.7788036   .3105837    -2.51   0.012    -1.387537   -.1700707
            div9 |   -.411922     .32351    -1.27   0.203     -1.04599    .2221459
           _cons |   6.564912   1.069266     6.14   0.000      4.46919    8.660634
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   1.133709   .1548009     7.32   0.000     .8303045    1.437113
           l_pop |   .2021709    .122246     1.65   0.098    -.0374268    .4417685
elevat_range_msa |  -.0278609   .0915127    -0.30   0.761    -.2072225    .1515006
  ruggedness_msa |   8.950256   3.938277     2.27   0.023     1.231374    16.66914
      heating_dd |  -.0243112   .0053137    -4.58   0.000    -.0347258   -.0138966
      cooling_dd |  -.0458606   .0142687    -3.21   0.001    -.0738268   -.0178943
          sprawl |  -.0009387   .0046424    -0.20   0.840    -.0100376    .0081602
            div2 |  -.2696107   .3021849    -0.89   0.372    -.8618822    .3226609
            div3 |  -.0413467   .3222035    -0.13   0.898    -.6728541    .5901606
            div4 |  -.1613294   .3607954    -0.45   0.655    -.8684753    .5458166
            div5 |  -.2857291   .3129321    -0.91   0.361    -.8990648    .3276066
            div6 |  -.3462245    .344047    -1.01   0.314    -1.020544    .3280952
            div7 |  -.0168283   .3397715    -0.05   0.960    -.6827683    .6491117
            div8 |  -.6213525   .4754301    -1.31   0.191    -1.553178    .3104733
            div9 |   -.598708   .3696822    -1.62   0.105    -1.323272    .1258558
           _cons |   6.784589   1.238505     5.48   0.000     4.357165    9.212014
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .9971827   .1851909     5.38   0.000     .6342152     1.36015
           l_pop |   .3692572   .1388602     2.66   0.008     .0970962    .6414182
elevat_range_msa |  -.0624976   .1420399    -0.44   0.660    -.3408906    .2158955
  ruggedness_msa |   12.78449   5.899217     2.17   0.030     1.222232    24.34674
      heating_dd |   -.010573   .0089378    -1.18   0.237    -.0280908    .0069448
      cooling_dd |  -.0039815    .021667    -0.18   0.854    -.0464481     .038485
          sprawl |   .0023883   .0037334     0.64   0.522    -.0049291    .0097057
            div2 |  -.3053577   .2879282    -1.06   0.289    -.8696866    .2589712
            div3 |    .009138   .2906395     0.03   0.975    -.5605049     .578781
            div4 |  -.1188421   .3035678    -0.39   0.695    -.7138241    .4761399
            div5 |  -.2678049   .3620878    -0.74   0.460    -.9774839    .4418741
            div6 |  -.4015621   .3722508    -1.08   0.281     -1.13116    .3280362
            div7 |  -.1650343   .3572958    -0.46   0.644    -.8653212    .5352526
            div8 |  -.2072431   .3380116    -0.61   0.540    -.8697337    .4552475
            div9 |  -.3556285   .4411018    -0.81   0.420    -1.220172     .508915
           _cons |   4.397687   1.373905     3.20   0.001     1.704882    7.090491
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   1.046986   .3007452     3.48   0.000     .4575366    1.636436
           l_pop |   .2505806   .2287798     1.10   0.273    -.1978195    .6989807
elevat_range_msa |   .0674278   .0930456     0.72   0.469    -.1149381    .2497938
  ruggedness_msa |   3.809859   4.279261     0.89   0.373    -4.577339    12.19706
      heating_dd |  -.0021102   .0094936    -0.22   0.824    -.0207173    .0164969
      cooling_dd |   .0150158   .0198955     0.75   0.450    -.0239787    .0540102
          sprawl |   .0029442   .0049968     0.59   0.556    -.0068494    .0127377
            div2 |  -.4730571   1.467937    -0.32   0.747     -3.35016    2.404046
            div3 |  -.1854474   1.641762    -0.11   0.910    -3.403241    3.032347
            div4 |  -.3889611   1.687325    -0.23   0.818    -3.696058    2.918136
            div5 |  -.3390211   1.488141    -0.23   0.820    -3.255723    2.577681
            div6 |  -.5817886   1.584267    -0.37   0.713    -3.686895    2.523318
            div7 |  -.4315369   1.540597    -0.28   0.779    -3.451052    2.587978
            div8 |  -.6679221   1.679148    -0.40   0.691    -3.958992    2.623147
            div9 |  -.5399534   1.478614    -0.37   0.715    -3.437983    2.358076
           _cons |   5.346623    2.17652     2.46   0.014     1.080722    9.612523
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl div2
            div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            8.293                 2.500
Constant effect |            1.776                 2.798
Dominance       |            0.000                 2.579
Exogeneity      |            1.753                 2.592
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90)  

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            8.293                 2.402
Constant effect |            1.776                 2.268
Dominance       |            0.000                 2.408
Exogeneity      |            1.753                 2.241
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
. * Model 4 *
. 
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_1983", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    3485.17
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9334
                                                  Root MSE        =     .36041

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.153168   .1308592     8.81   0.000     .8966889    1.409648
           l_pop |   .2637207   .1170578     2.25   0.024     .0342915    .4931498
   S_somecollege |    .676887    .587283     1.15   0.249    -.4741664    1.827941
   l_mean_income |  -.0604615   .5799422    -0.10   0.917    -1.197127    1.076204
          S_poor |   .4201981   .7063741     0.59   0.552    -.9642696    1.804666
         S_manuf |   .3357823   .3840518     0.87   0.382    -.4169454     1.08851
            div2 |  -.2028549   .2213102    -0.92   0.359     -.636615    .2309051
            div3 |  -.0193329   .2614817    -0.07   0.941    -.5318276    .4931618
            div4 |  -.1493699   .2644041    -0.56   0.572    -.6675924    .3688525
            div5 |  -.1822386   .2350294    -0.78   0.438    -.6428877    .2784105
            div6 |  -.3573986   .2644655    -1.35   0.177    -.8757414    .1609443
            div7 |  -.1116564   .2830879    -0.39   0.693    -.6664985    .4431856
            div8 |  -.4417695   .3133119    -1.41   0.159     -1.05585    .1723107
            div9 |  -.3188542   .2945992    -1.08   0.279     -.896258    .2585496
elevat_range_msa |  -.0754879   .0627373    -1.20   0.229    -.1984509     .047475
  ruggedness_msa |   7.316705   3.740218     1.96   0.050     -.013988     14.6474
      heating_dd |  -.0153037   .0057414    -2.67   0.008    -.0265566   -.0040508
      cooling_dd |  -.0221265   .0119574    -1.85   0.064    -.0455625    .0013095
          sprawl |   .0020474   .0042186     0.49   0.627    -.0062208    .0103156
           _cons |   5.261053   5.030754     1.05   0.296    -4.599044    15.12115
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop S_somecoll
> ege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl if year == "_1983",  quantile(10
>  25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(95) = 9520.72
                                                       Prob > chi2   =  0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |     1.2367   .1301835     9.50   0.000     .9815452    1.491855
           l_pop |   .2270854   .1254489     1.81   0.070    -.0187899    .4729607
   S_somecollege |   1.391862   .7444626     1.87   0.062    -.0672579    2.850982
   l_mean_income |  -.1372358   .7407995    -0.19   0.853    -1.589176    1.314705
          S_poor |  -.6167296   1.027016    -0.60   0.548    -2.629644    1.396185
         S_manuf |   .3005476    .460585     0.65   0.514    -.6021825    1.203278
            div2 |   .4591072   .4839293     0.95   0.343    -.4893768    1.407591
            div3 |   .6147787   .4970776     1.24   0.216    -.3594754    1.589033
            div4 |    .496379   .4651789     1.07   0.286     -.415355    1.408113
            div5 |   .4359096   .5472582     0.80   0.426    -.6366969    1.508516
            div6 |   .4501071   .5103638     0.88   0.378    -.5501875    1.450402
            div7 |   .4947633   .5554319     0.89   0.373    -.5938631     1.58339
            div8 |   .0885095    .547727     0.16   0.872    -.9850157    1.162035
            div9 |   .4687404   .6753445     0.69   0.488    -.8549105    1.792391
elevat_range_msa |  -.1642416   .0958266    -1.71   0.087    -.3520584    .0235752
  ruggedness_msa |   8.641217   4.958354     1.74   0.081    -1.076978    18.35941
      heating_dd |  -.0118843    .006837    -1.74   0.082    -.0252847     .001516
      cooling_dd |  -.0152466   .0132874    -1.15   0.251    -.0412895    .0107963
          sprawl |   .0118333   .0058776     2.01   0.044     .0003133    .0233532
           _cons |   4.242079     6.6743     0.64   0.525    -8.839309    17.32347
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.263875   .1225088    10.32   0.000     1.023762    1.503988
           l_pop |    .164455   .1027169     1.60   0.109    -.0368664    .3657764
   S_somecollege |    .320588   .7355141     0.44   0.663    -1.120993    1.762169
   l_mean_income |   .0040186   .6513679     0.01   0.995    -1.272639    1.280676
          S_poor |    .779713   .9499987     0.82   0.412     -1.08225    2.641676
         S_manuf |   .5337666   .4701087     1.14   0.256    -.3876295    1.455163
            div2 |  -.1015264   .2764615    -0.37   0.713     -.643381    .4403283
            div3 |   .1295077   .2860219     0.45   0.651    -.4310849    .6901003
            div4 |   .0423539   .2804146     0.15   0.880    -.5072486    .5919565
            div5 |  -.2001117   .3009458    -0.66   0.506    -.7899546    .3897311
            div6 |  -.3733841   .3145795    -1.19   0.235    -.9899485    .2431804
            div7 |  -.0439221   .3241278    -0.14   0.892    -.6792009    .5913567
            div8 |  -.5381384     .32827    -1.64   0.101    -1.181536     .105259
            div9 |  -.2827377   .3624968    -0.78   0.435    -.9932183     .427743
elevat_range_msa |  -.0863591   .0710723    -1.22   0.224    -.2256583      .05294
  ruggedness_msa |   8.875094   3.719879     2.39   0.017     1.584266    16.16592
      heating_dd |   -.021447   .0059963    -3.58   0.000    -.0331996   -.0096944
      cooling_dd |  -.0327544   .0142417    -2.30   0.021    -.0606676   -.0048412
          sprawl |   .0038586   .0048996     0.79   0.431    -.0057444    .0134616
           _cons |   5.262585   6.115803     0.86   0.390    -6.724168    17.24934
-----------------+----------------------------------------------------------------
q50              |
            l_ln |    1.02243   .1002911    10.19   0.000     .8258626    1.218997
           l_pop |   .3584973   .0939504     3.82   0.000     .1743579    .5426366
   S_somecollege |   .6441237   .7326962     0.88   0.379    -.7919345    2.080182
   l_mean_income |  -.6363219   .3918075    -1.62   0.104     -1.40425    .1316066
          S_poor |   .3370352   .6243616     0.54   0.589    -.8866911    1.560761
         S_manuf |   .0972919   .4026768     0.24   0.809    -.6919401     .886524
            div2 |  -.1741114   .1676227    -1.04   0.299    -.5026459    .1544231
            div3 |   .0361617   .1751463     0.21   0.836    -.3071187    .3794421
            div4 |   -.070381   .2073888    -0.34   0.734    -.4768557    .3360937
            div5 |  -.2675027   .1769729    -1.51   0.131    -.6143633    .0793579
            div6 |  -.3322021   .1874124    -1.77   0.076    -.6995237    .0351195
            div7 |  -.0558292   .1924245    -0.29   0.772    -.4329744     .321316
            div8 |  -.3926141   .2811749    -1.40   0.163    -.9437068    .1584785
            div9 |  -.3980297   .2584145    -1.54   0.123    -.9045127    .1084534
elevat_range_msa |  -.0755384      .0928    -0.81   0.416     -.257423    .1063461
  ruggedness_msa |   10.23121   3.344609     3.06   0.002     3.675894    16.78652
      heating_dd |  -.0218501   .0053674    -4.07   0.000      -.03237   -.0113302
      cooling_dd |   -.035697    .011916    -3.00   0.003    -.0590518   -.0123421
          sprawl |  -.0005949    .003411    -0.17   0.862    -.0072804    .0060905
           _cons |   11.28062   3.772171     2.99   0.003     3.887302    18.67394
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8905388   .1894443     4.70   0.000     .5192349    1.261843
           l_pop |   .4595652   .1401531     3.28   0.001     .1848702    .7342603
   S_somecollege |   -.414911   .8213035    -0.51   0.613    -2.024636    1.194814
   l_mean_income |   .0852503   .8040699     0.11   0.916    -1.490698    1.661198
          S_poor |   .7870886   .9809304     0.80   0.422      -1.1355    2.709677
         S_manuf |  -.5246881   .6006424    -0.87   0.382    -1.701925    .6525494
            div2 |  -.2365068   .1867542    -1.27   0.205    -.6025384    .1295247
            div3 |   .0672897   .2041925     0.33   0.742    -.3329203    .4674997
            div4 |  -.1080731   .2599263    -0.42   0.678    -.6175193     .401373
            div5 |  -.1816921   .2843656    -0.64   0.523    -.7390384    .3756542
            div6 |  -.3115645    .254231    -1.23   0.220    -.8098481    .1867191
            div7 |  -.1315752   .2700381    -0.49   0.626    -.6608402    .3976898
            div8 |  -.2548993   .2981517    -0.85   0.393    -.8392659    .3294673
            div9 |  -.2772811   .2880499    -0.96   0.336    -.8418485    .2872864
elevat_range_msa |   .0001252   .1212425     0.00   0.999    -.2375058    .2377562
  ruggedness_msa |   7.688116    5.31642     1.45   0.148    -2.731875    18.10811
      heating_dd |  -.0066796   .0083195    -0.80   0.422    -.0229854    .0096262
      cooling_dd |   .0008796   .0186988     0.05   0.962    -.0357695    .0375286
          sprawl |   .0023678   .0047766     0.50   0.620    -.0069943    .0117298
           _cons |   2.980403   7.431625     0.40   0.688    -11.58531    17.54612
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8752442   .2004557     4.37   0.000     .4823582     1.26813
           l_pop |    .416453   .1311261     3.18   0.001     .1594506    .6734555
   S_somecollege |   .6950382   .9789603     0.71   0.478    -1.223689    2.613765
   l_mean_income |  -.2338604   1.493882    -0.16   0.876    -3.161816    2.694095
          S_poor |   .1867455   1.553815     0.12   0.904    -2.858676    3.232167
         S_manuf |   .6179568    .778717     0.79   0.427    -.9083005    2.144214
            div2 |  -.1336619   .1947248    -0.69   0.492    -.5153155    .2479917
            div3 |     .25303   .2066317     1.22   0.221    -.1519606    .6580206
            div4 |   .0569526   .3742645     0.15   0.879    -.6765923    .7904975
            div5 |  -.1472546   .3308079    -0.45   0.656    -.7956261    .5011168
            div6 |  -.2528595   .2244067    -1.13   0.260    -.6926885    .1869695
            div7 |  -.1502569   .2547749    -0.59   0.555    -.6496065    .3490927
            div8 |   -.062376   .4042508    -0.15   0.877    -.8546931    .7299411
            div9 |  -.1457797   .2961576    -0.49   0.623    -.7262379    .4346785
elevat_range_msa |    .043004   .1229519     0.35   0.727    -.1979774    .2839853
  ruggedness_msa |   1.539486   6.239893     0.25   0.805    -10.69048    13.76945
      heating_dd |  -.0074737   .0089752    -0.83   0.405    -.0250648    .0101173
      cooling_dd |   .0095167   .0166276     0.57   0.567    -.0230728    .0421062
          sprawl |   .0069958   .0044948     1.56   0.120    -.0018139    .0158055
           _cons |   6.132391   14.71274     0.42   0.677    -22.70405    34.96883
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           13.487                 2.235
Constant effect |            2.669                 2.098
Dominance       |            0.000                 2.311
Exogeneity      |            1.477                 2.345
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           13.487                 1.990
Constant effect |            2.669                 1.907
Dominance       |            0.000                 2.175
Exogeneity      |            1.477                 1.944
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
.                 
. * Model 5 * 
. 
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_1983", robust 
note: l_pop80 omitted because of collinearity.

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(25)   =    3564.51
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9294
                                                  Root MSE        =     .37116

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.209407   .1560418     7.75   0.000     .9035702    1.515243
           l_pop |   .8076052   .3975979     2.03   0.042     .0283277    1.586883
         l_pop80 |          0  (omitted)
         l_pop70 |  -.2933245   .7136873    -0.41   0.681    -1.692126    1.105477
         l_pop60 |  -.2562645   .5763745    -0.44   0.657    -1.385938    .8734088
         l_pop50 |   -.052708   .3657762    -0.14   0.885    -.7696161    .6642001
         l_pop40 |  -.2003127   .4060961    -0.49   0.622    -.9962465    .5956211
         l_pop30 |   .2940406   .3098573     0.95   0.343    -.3132686    .9013498
         l_pop20 |  -.0710513   .1380884    -0.51   0.607    -.3416996     .199597
   S_somecollege |   .0005674   .8781519     0.00   0.999    -1.720579    1.721714
   l_mean_income |   .1600142   .6332394     0.25   0.801    -1.081112    1.401141
          S_poor |   .7826639   .7880007     0.99   0.321    -.7617891    2.327117
         S_manuf |   .3835071   .4082705     0.94   0.348    -.4166883    1.183702
            div2 |  -.2694866    .265279    -1.02   0.310    -.7894238    .2504506
            div3 |  -.1039546    .314631    -0.33   0.741    -.7206201    .5127109
            div4 |  -.1827818   .2984389    -0.61   0.540    -.7677114    .4021477
            div5 |  -.2647555   .2790505    -0.95   0.343    -.8116845    .2821734
            div6 |    -.42869   .3072031    -1.40   0.163    -1.030797    .1734171
            div7 |   -.139173   .3174915    -0.44   0.661     -.761445    .4830989
            div8 |  -.5764346   .3820095    -1.51   0.131    -1.325159    .1722902
            div9 |  -.4010393   .3629437    -1.10   0.269    -1.112396    .3103173
elevat_range_msa |  -.0949136   .0625016    -1.52   0.129    -.2174144    .0275872
  ruggedness_msa |   6.426562    3.75422     1.71   0.087    -.9315742     13.7847
      heating_dd |   -.017303   .0063722    -2.72   0.007    -.0297923   -.0048137
      cooling_dd |  -.0348996   .0137612    -2.54   0.011    -.0618711   -.0079281
          sprawl |   .0008905   .0047222     0.19   0.850    -.0083649    .0101459
           _cons |   3.551531   5.358626     0.66   0.507    -6.951183    14.05424
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege
            l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9
            elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
            l_rail1898 l_hwy1947

.                 
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop l_pop80 l_
> pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_
> manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heati
> ng_dd cooling_dd sprawl if year == "_1983",  quantile(10 25 50 75 90) vce(robust) 
note: l_pop80 omitted because of collinearity.

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      228
Estimator: Inverse quantile regression               Wald chi2(125) = 13668.37
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.226848   .1848892     6.64   0.000     .8644716    1.589224
           l_pop |   .5746107   .4854827     1.18   0.237    -.3769178    1.526139
         l_pop80 |          0  (omitted)
         l_pop70 |  -.2895353   .6757945    -0.43   0.668    -1.614068    1.034998
         l_pop60 |  -.3214891   1.269259    -0.25   0.800    -2.809192    2.166214
         l_pop50 |   .1664823   1.159406     0.14   0.886    -2.105912    2.438877
         l_pop40 |   .1474071   .8103301     0.18   0.856    -1.440811    1.735625
         l_pop30 |   .0653202   .8374472     0.08   0.938    -1.576046    1.706686
         l_pop20 |  -.1305973   .3963878    -0.33   0.742    -.9075031    .6463086
   S_somecollege |   1.523274   1.118832     1.36   0.173    -.6695969    3.716144
   l_mean_income |  -.0112793   .8360794    -0.01   0.989    -1.649965    1.627406
          S_poor |  -.5900283   1.592622    -0.37   0.711    -3.711511    2.531454
         S_manuf |   .1973104   .6376291     0.31   0.757     -1.05242     1.44704
            div2 |   .4594008    .546756     0.84   0.401    -.6122212    1.531023
            div3 |   .5806274   .5753121     1.01   0.313    -.5469636    1.708218
            div4 |   .4184963   .5033656     0.83   0.406    -.5680822    1.405075
            div5 |   .3751478   .6581436     0.57   0.569    -.9147898    1.665085
            div6 |   .3711348   .6413127     0.58   0.563     -.885815    1.628085
            div7 |   .4984662   .6764723     0.74   0.461    -.8273951    1.824328
            div8 |   -.073056   .6258217    -0.12   0.907    -1.299644    1.153532
            div9 |   .3153124   .8697199     0.36   0.717    -1.389307    2.019932
elevat_range_msa |  -.1301602   .1473421    -0.88   0.377    -.4189453     .158625
  ruggedness_msa |   8.248261   6.291571     1.31   0.190    -4.082991    20.57951
      heating_dd |  -.0138656   .0095855    -1.45   0.148    -.0326527    .0049216
      cooling_dd |  -.0223761   .0261748    -0.85   0.393    -.0736777    .0289256
          sprawl |   .0108192   .0090475     1.20   0.232    -.0069135    .0285519
           _cons |   3.468998    7.51613     0.46   0.644    -11.26235    18.20034
-----------------+----------------------------------------------------------------
q25              |
            l_ln |    1.34829    .121689    11.08   0.000     1.109784    1.586796
           l_pop |   .8456827   .3782672     2.24   0.025     .1042926    1.587073
         l_pop80 |          0  (omitted)
         l_pop70 |  -.6541797   .7504177    -0.87   0.383    -2.124971    .8166119
         l_pop60 |   .0312738   .8861728     0.04   0.972    -1.705593     1.76814
         l_pop50 |  -.3267943   .5622544    -0.58   0.561    -1.428793    .7752041
         l_pop40 |   .0739672    .386639     0.19   0.848    -.6838313    .8317658
         l_pop30 |   .1360165   .3133373     0.43   0.664    -.4781134    .7501463
         l_pop20 |   .0019539   .1311237     0.01   0.988    -.2550437    .2589516
   S_somecollege |  -.7839536   1.032489    -0.76   0.448    -2.807595    1.239688
   l_mean_income |   .5335078   .5821722     0.92   0.359    -.6075289    1.674544
          S_poor |   1.363955   .8847459     1.54   0.123    -.3701148    3.098025
         S_manuf |   .4360182   .3914874     1.11   0.265    -.3312829    1.203319
            div2 |  -.1741461    .307503    -0.57   0.571    -.7768409    .4285486
            div3 |   .0402707   .3108514     0.13   0.897    -.5689869    .6495282
            div4 |    -.00701   .3176055    -0.02   0.982    -.6295054    .6154855
            div5 |  -.1924119   .3324082    -0.58   0.563    -.8439201    .4590962
            div6 |  -.4453595   .3443529    -1.29   0.196    -1.120279    .2295597
            div7 |   .0186694    .359035     0.05   0.959    -.6850262    .7223651
            div8 |  -.7110359   .3656095    -1.94   0.052    -1.427617    .0055456
            div9 |  -.1638979   .4568474    -0.36   0.720    -1.059302    .7315066
elevat_range_msa |  -.0987236   .0792091    -1.25   0.213    -.2539705    .0565233
  ruggedness_msa |   9.475848   3.901593     2.43   0.015     1.828867    17.12283
      heating_dd |  -.0194392   .0074339    -2.61   0.009    -.0340093    -.004869
      cooling_dd |  -.0357845   .0183258    -1.95   0.051    -.0717024    .0001335
          sprawl |   .0007768   .0057587     0.13   0.893      -.01051    .0120635
           _cons |   .4038109   5.294806     0.08   0.939    -9.973818    10.78144
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   1.311777   .1364573     9.61   0.000     1.044326    1.579228
           l_pop |    1.02912   .5213414     1.97   0.048     .0073098     2.05093
         l_pop80 |          0  (omitted)
         l_pop70 |  -.2083028   .8742819    -0.24   0.812    -1.921864    1.505258
         l_pop60 |  -1.078268    .703102    -1.53   0.125    -2.456323    .2997864
         l_pop50 |   .6950814   .4014412     1.73   0.083     -.091729    1.481892
         l_pop40 |  -.5888485   .4451399    -1.32   0.186    -1.461307    .2836097
         l_pop30 |   .2399446   .3418397     0.70   0.483     -.430049    .9099382
         l_pop20 |   .0049942   .1079852     0.05   0.963    -.2066529    .2166413
   S_somecollege |  -.3897208   1.028946    -0.38   0.705    -2.406418    1.626976
   l_mean_income |   .1504383   .7149951     0.21   0.833    -1.250926    1.551803
          S_poor |   .8121739   1.001336     0.81   0.417    -1.150408    2.774756
         S_manuf |   .6006334   .4903454     1.22   0.221     -.360426    1.561693
            div2 |  -.4823351    .259536    -1.86   0.063    -.9910164    .0263462
            div3 |  -.3438868   .2890221    -1.19   0.234    -.9103597    .2225861
            div4 |  -.4631498   .2832027    -1.64   0.102    -1.018217    .0919174
            div5 |  -.6395404   .2760115    -2.32   0.020    -1.180513   -.0985678
            div6 |  -.7712192   .3239408    -2.38   0.017    -1.406131   -.1363069
            div7 |  -.4216924   .3406943    -1.24   0.216    -1.089441    .2460562
            div8 |  -1.274346   .4590427    -2.78   0.006    -2.174053   -.3746385
            div9 |  -1.052986   .4845574    -2.17   0.030    -2.002701   -.1032709
elevat_range_msa |  -.0191124   .0910128    -0.21   0.834    -.1974941    .1592694
  ruggedness_msa |   7.934048   3.473668     2.28   0.022     1.125783    14.74231
      heating_dd |  -.0260601   .0069907    -3.73   0.000    -.0397616   -.0123586
      cooling_dd |  -.0536187   .0157986    -3.39   0.001    -.0845833   -.0226541
          sprawl |  -.0042629   .0063803    -0.67   0.504     -.016768    .0082422
           _cons |    5.76512   6.259133     0.92   0.357    -6.502556    18.03279
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .8949363   .2579221     3.47   0.001     .3894182    1.400454
           l_pop |   1.493105   .6716136     2.22   0.026     .1767667    2.809444
         l_pop80 |          0  (omitted)
         l_pop70 |  -1.116073   1.043419    -1.07   0.285    -3.161136    .9289907
         l_pop60 |  -.0598822    .752267    -0.08   0.937    -1.534298    1.414534
         l_pop50 |   .2717805   .4620096     0.59   0.556    -.6337418    1.177303
         l_pop40 |  -.5335028   .5223438    -1.02   0.307    -1.557278    .4902722
         l_pop30 |   .4583876   .6705374     0.68   0.494    -.8558415    1.772617
         l_pop20 |  -.0892809   .3931954    -0.23   0.820    -.8599297     .681368
   S_somecollege |  -1.041955   .7912974    -1.32   0.188     -2.59287    .5089588
   l_mean_income |   .6419405   .5409031     1.19   0.235      -.41821    1.702091
          S_poor |   1.317634   .7561042     1.74   0.081    -.1643025    2.799571
         S_manuf |   -.670806   .3038295    -2.21   0.027    -1.266301   -.0753112
            div2 |  -.1832568   .1511496    -1.21   0.225    -.4795045    .1129909
            div3 |   .0548064   .1734353     0.32   0.752    -.2851204    .3947333
            div4 |  -.1280156    .175582    -0.73   0.466    -.4721501    .2161189
            div5 |  -.1792088   .2036225    -0.88   0.379    -.5783016     .219884
            div6 |   -.357094   .1805337    -1.98   0.048    -.7109335   -.0032544
            div7 |  -.1913653   .2298511    -0.83   0.405    -.6418652    .2591346
            div8 |  -.5254721   .3605085    -1.46   0.145    -1.232056    .1811116
            div9 |  -.3867479   .2837837    -1.36   0.173    -.9429537    .1694579
elevat_range_msa |  -.0175498   .1350717    -0.13   0.897    -.2822855    .2471858
  ruggedness_msa |   4.297595   4.333493     0.99   0.321    -4.195896    12.79108
      heating_dd |   -.007403   .0080289    -0.92   0.357    -.0231394    .0083334
      cooling_dd |  -.0106202   .0230792    -0.46   0.645    -.0558547    .0346142
          sprawl |   -.000259   .0037911    -0.07   0.946    -.0076895    .0071715
           _cons |   -1.89381   5.198708    -0.36   0.716    -12.08309    8.295471
-----------------+----------------------------------------------------------------
q90              |
            l_ln |    .865096   .1680607     5.15   0.000      .535703    1.194489
           l_pop |   .1540381   .6612719     0.23   0.816    -1.142031    1.450107
         l_pop80 |          0  (omitted)
         l_pop70 |   1.298061   1.207132     1.08   0.282    -1.067874    3.663996
         l_pop60 |  -.9107536   .6798516    -1.34   0.180    -2.243238     .421731
         l_pop50 |   .0129208   .5222358     0.02   0.980    -1.010643    1.036484
         l_pop40 |  -.6422782    .447313    -1.44   0.151    -1.518996    .2344392
         l_pop30 |    .373763   .4202158     0.89   0.374    -.4498447    1.197371
         l_pop20 |   .0828671   .2570848     0.32   0.747    -.4210098     .586744
   S_somecollege |  -.7345064   .7423962    -0.99   0.322    -2.189576    .7205634
   l_mean_income |   .4593559   .4746559     0.97   0.333    -.4709525    1.389664
          S_poor |   .9017421   .6900455     1.31   0.191    -.4507222    2.254206
         S_manuf |   .1335405   .4473973     0.30   0.765    -.7433422    1.010423
            div2 |    -.24541   .1513606    -1.62   0.105    -.5420713    .0512513
            div3 |   .0725711   .1692497     0.43   0.668    -.2591522    .4042944
            div4 |  -.0438215   .1822492    -0.24   0.810    -.4010234    .3133803
            div5 |  -.1645565   .1850766    -0.89   0.374       -.5273     .198187
            div6 |  -.2807842   .1904058    -1.47   0.140    -.6539728    .0924043
            div7 |  -.1744215   .2082195    -0.84   0.402    -.5825241    .2336812
            div8 |  -.2297653   .2759454    -0.83   0.405    -.7706082    .3110777
            div9 |  -.3283444   .2867793    -1.14   0.252    -.8904215    .2337327
elevat_range_msa |   .0092725    .100669     0.09   0.927     -.188035    .2065801
  ruggedness_msa |   6.265163   3.239485     1.93   0.053    -.0841118    12.61444
      heating_dd |   -.006292   .0063774    -0.99   0.324    -.0187916    .0062075
      cooling_dd |   .0068634   .0165638     0.41   0.679     -.025601    .0393277
          sprawl |   .0060653   .0036425     1.67   0.096    -.0010738    .0132045
           _cons |  -.0208739   4.413961    -0.00   0.996    -8.672078     8.63033
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege
            l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9
            elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl l_rail1898
            l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            9.613                 2.391
Constant effect |            1.598                 2.121
Dominance       |            0.000                 2.426
Exogeneity      |            2.831                 2.431
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            9.613                 2.089
Constant effect |            1.598                 1.945
Dominance       |            0.000                 2.079
Exogeneity      |            2.831                 2.105
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
.                 
.                 
.                 
.                 
.                 
. *** Decade 2 ('90s) *** 
. 
. 
. use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
. 
. 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 keep if year == "_1993" 
(456 observations deleted)

. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 
. 
. * Model 1 *
. 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_199
> 3", robust       

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =     737.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8644
                                                  Root MSE        =     .47683

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.333483    .049112    27.15   0.000     1.237225    1.429741
       _cons |   6.563724   .3314169    19.81   0.000     5.914159    7.213289
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_199
> 3",  quantile(10 25 50 75 90) vce(robust)   

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(5)  = 2288.70
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.539207   .0516615    29.79   0.000     1.437952    1.640461
       _cons |    4.52883   .4056449    11.16   0.000     3.733781    5.323879
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.398125   .0427782    32.68   0.000     1.314282    1.481969
       _cons |   5.953288   .3109281    19.15   0.000      5.34388    6.562696
-------------+----------------------------------------------------------------
q50          |
        l_ln |     1.2739   .0338162    37.67   0.000     1.207621    1.340178
       _cons |   7.082117   .2350634    30.13   0.000     6.621401    7.542833
-------------+----------------------------------------------------------------
q75          |
        l_ln |    1.23019   .0322368    38.16   0.000     1.167007    1.293373
       _cons |   7.507295   .2228344    33.69   0.000     7.070548    7.944043
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.174139   .0860821    13.64   0.000     1.005421    1.342857
       _cons |    8.19834   .5920531    13.85   0.000     7.037937    9.358743
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           40.666                 2.282
Constant effect |            5.480                 2.158
Dominance       |            0.000                 2.182
Exogeneity      |            1.439                 2.390
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           40.666                 1.924
Constant effect |            5.480                 1.942
Dominance       |            0.000                 1.945
Exogeneity      |            1.439                 2.188
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. * Model 2 *
. 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_1993", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    2268.59
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9265
                                                  Root MSE        =     .35102

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .9750698   .1281312     7.61   0.000     .7239374    1.226202
       l_pop |   .3682688   .0958781     3.84   0.000     .1803512    .5561863
       _cons |   4.202293    .462304     9.09   0.000     3.296194    5.108392
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                   
.                  ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop if year =
> = "_1993",  quantile(10 25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(10) = 5396.78
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   1.100122   .0874127    12.59   0.000     .9287962    1.271448
       l_pop |   .4024414   .0589466     6.83   0.000     .2869083    .5179745
       _cons |   2.719939   .3589672     7.58   0.000     2.016376    3.423502
-------------+----------------------------------------------------------------
q25          |
        l_ln |   .9894475   .1266084     7.82   0.000     .7412996    1.237595
       l_pop |   .3623657   .1019381     3.55   0.000     .1625707    .5621606
       _cons |   4.017169   .5476917     7.33   0.000     2.943713    5.090625
-------------+----------------------------------------------------------------
q50          |
        l_ln |   .9050351   .1359384     6.66   0.000     .6386008    1.171469
       l_pop |   .4091115   .1096865     3.73   0.000     .1941299    .6240932
       _cons |   4.196829   .5866592     7.15   0.000     3.046998     5.34666
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .8425769   .1474609     5.71   0.000     .5535589    1.131595
       l_pop |   .4151725   .1345249     3.09   0.002     .1515085    .6788366
       _cons |   4.759604     .83745     5.68   0.000     3.118232    6.400976
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .8528283   .0885087     9.64   0.000     .6793543    1.026302
       l_pop |   .3342133   .0751238     4.45   0.000     .1869733    .4814534
       _cons |   5.745333   .4307568    13.34   0.000     4.901065    6.589601
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                  
.                  estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           14.376                 2.323
Constant effect |            1.708                 2.338
Dominance       |            0.000                 2.111
Exogeneity      |            2.102                 2.285
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                  estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           14.376                 2.059
Constant effect |            1.708                 2.115
Dominance       |            0.000                 1.983
Exogeneity      |            2.102                 1.857
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. * Model 3 *
. 
.                 ivregress liml l_vmt l_pop elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 div8 div9 (l_ln = l_rail1898 l_h
> wy1947) if year == "_1993", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    4016.76
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9343
                                                  Root MSE        =     .33189

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.127834   .1608668     7.01   0.000     .8125409    1.443127
           l_pop |   .2126386   .1298594     1.64   0.102    -.0418811    .4671583
elevat_range_msa |  -.0105343   .0650254    -0.16   0.871    -.1379817     .116913
  ruggedness_msa |   6.723697   3.698433     1.82   0.069    -.5250991    13.97249
      heating_dd |  -.0160764   .0047356    -3.39   0.001     -.025358   -.0067947
      cooling_dd |  -.0249773   .0093188    -2.68   0.007    -.0432417   -.0067129
          sprawl |  -.0009375   .0034764    -0.27   0.787    -.0077511    .0058761
            div2 |  -.2047742   .1030457    -1.99   0.047    -.4067402   -.0028083
            div3 |   .0368745   .1107627     0.33   0.739    -.1802163    .2539654
            div4 |  -.1130231   .1407965    -0.80   0.422    -.3889791     .162933
            div5 |  -.0815081   .1413372    -0.58   0.564    -.3585238    .1955077
            div6 |  -.2018811   .1443589    -1.40   0.162    -.4848193    .0810572
            div7 |  -.2466525   .1624922    -1.52   0.129    -.5651313    .0718263
            div8 |  -.5230422   .2259541    -2.31   0.021    -.9659042   -.0801803
            div9 |  -.3902554   .2059464    -1.89   0.058    -.7939029     .013392
           _cons |   6.440649   .9589687     6.72   0.000     4.561104    8.320193
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
            div2 div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 di
> v8 div9 if year == "_1993",  quantile(10 25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      228
Estimator: Inverse quantile regression                Wald chi2(75) = 12065.07
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.588671   .3818061     4.16   0.000     .8403443    2.336997
           l_pop |  -.1333662    .312085    -0.43   0.669    -.7450415    .4783091
elevat_range_msa |   -.079102   .1638182    -0.48   0.629    -.4001798    .2419757
  ruggedness_msa |   19.63443   7.863143     2.50   0.013     4.222952    35.04591
      heating_dd |  -.0295148   .0179112    -1.65   0.099    -.0646202    .0055905
      cooling_dd |  -.0434094   .0325066    -1.34   0.182    -.1071212    .0203024
          sprawl |   -.005636   .0079013    -0.71   0.476    -.0211223    .0098502
            div2 |   .2549651   .6347952     0.40   0.688    -.9892106    1.499141
            div3 |    .446076   .6272809     0.71   0.477     -.783372    1.675524
            div4 |   .2110844   .5956049     0.35   0.723    -.9562797    1.378448
            div5 |   .1353533   .7271432     0.19   0.852    -1.289821    1.560528
            div6 |   .0727105   .7122668     0.10   0.919    -1.323307    1.468728
            div7 |   .0251743   .7446848     0.03   0.973    -1.434381     1.48473
            div8 |  -.8653238   .6517202    -1.33   0.184    -2.142672    .4120242
            div9 |  -.3650816   .9621035    -0.38   0.704     -2.25077    1.520607
           _cons |   8.064279    2.86985     2.81   0.005     2.439476    13.68908
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.225852   .1690808     7.25   0.000     .8944595    1.557244
           l_pop |   .1104097   .1382675     0.80   0.425    -.1605896    .3814089
elevat_range_msa |  -.0445175   .0942523    -0.47   0.637    -.2292485    .1402135
  ruggedness_msa |    9.68467   6.852991     1.41   0.158    -3.746946    23.11629
      heating_dd |  -.0295831   .0086113    -3.44   0.001    -.0464609   -.0127052
      cooling_dd |  -.0432953    .017213    -2.52   0.012    -.0770322   -.0095583
          sprawl |  -.0016711    .003424    -0.49   0.626    -.0083821    .0050399
            div2 |  -.3459716   .0804371    -4.30   0.000    -.5036255   -.1883178
            div3 |  -.1698277   .1297357    -1.31   0.191     -.424105    .0844496
            div4 |  -.4039097    .164826    -2.45   0.014    -.7269628   -.0808566
            div5 |  -.6049706    .221111    -2.74   0.006     -1.03834   -.1716009
            div6 |  -.6866538   .2226433    -3.08   0.002    -1.123027    -.250281
            div7 |  -.6798208   .2209933    -3.08   0.002     -1.11296   -.2466819
            div8 |  -1.233558   .3222932    -3.83   0.000    -1.865241   -.6018748
            div9 |   -.917952   .3745462    -2.45   0.014    -1.652049    -.183855
           _cons |   8.224021   1.279386     6.43   0.000      5.71647    10.73157
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9129688   .1799792     5.07   0.000     .5602161    1.265721
           l_pop |   .3561709   .1568149     2.27   0.023     .0488193    .6635226
elevat_range_msa |  -.0129416   .1167232    -0.11   0.912    -.2417148    .2158316
  ruggedness_msa |   5.195905   4.710241     1.10   0.270    -4.035997    14.42781
      heating_dd |  -.0183812   .0078242    -2.35   0.019    -.0337164    -.003046
      cooling_dd |  -.0331939   .0167406    -1.98   0.047     -.066005   -.0003829
          sprawl |  -.0021615   .0052436    -0.41   0.680    -.0124388    .0081158
            div2 |  -.2420616   .1316698    -1.84   0.066    -.5001296    .0160064
            div3 |   .0564282   .1398185     0.40   0.687     -.217611    .3304674
            div4 |  -.1146224   .1896084    -0.60   0.545    -.4862481    .2570032
            div5 |  -.1029713   .1960714    -0.53   0.599    -.4872642    .2813216
            div6 |  -.1173178   .1786787    -0.66   0.511    -.4675217    .2328861
            div7 |  -.2642736   .2249213    -1.17   0.240    -.7051112    .1765641
            div8 |  -.3618077   .3082342    -1.17   0.240    -.9659357    .2423203
            div9 |  -.4024104   .3147083    -1.28   0.201    -1.019227    .2144065
           _cons |   6.446388   1.336887     4.82   0.000     3.826138    9.066638
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .9206615   .1165084     7.90   0.000     .6923093    1.149014
           l_pop |   .3313675   .1016903     3.26   0.001     .1320583    .5306768
elevat_range_msa |  -.0075631   .0855842    -0.09   0.930     -.175305    .1601789
  ruggedness_msa |   5.748647   3.164739     1.82   0.069     -.454127    11.95142
      heating_dd |  -.0071176   .0046377    -1.53   0.125    -.0162074    .0019721
      cooling_dd |  -.0076347   .0103635    -0.74   0.461    -.0279469    .0126775
          sprawl |   .0002355   .0030793     0.08   0.939    -.0057998    .0062708
            div2 |  -.3715131   .1468372    -2.53   0.011    -.6593088   -.0837175
            div3 |  -.0170327    .146512    -0.12   0.907    -.3041909    .2701254
            div4 |  -.1565827   .1560635    -1.00   0.316    -.4624615    .1492962
            div5 |  -.0897775   .1780411    -0.50   0.614    -.4387317    .2591767
            div6 |  -.1807981   .1753535    -1.03   0.303    -.5244846    .1628884
            div7 |  -.1539172   .1974779    -0.78   0.436    -.5409668    .2331323
            div8 |  -.2836578   .2602746    -1.09   0.276    -.7937867    .2264712
            div9 |  -.2508429   .2694622    -0.93   0.352    -.7789791    .2772933
           _cons |   5.674818   .7410808     7.66   0.000     4.222327     7.12731
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   1.016406   .1238298     8.21   0.000     .7737038    1.259108
           l_pop |   .1900145    .111355     1.71   0.088    -.0282373    .4082664
elevat_range_msa |   .0201697   .1006148     0.20   0.841    -.1770318    .2173711
  ruggedness_msa |   4.048103   5.704972     0.71   0.478    -7.133438    15.22964
      heating_dd |  -.0047835   .0053046    -0.90   0.367    -.0151803    .0056134
      cooling_dd |   .0064211    .010861     0.59   0.554    -.0148661    .0277083
          sprawl |   .0007656   .0039403     0.19   0.846    -.0069573    .0084885
            div2 |  -.4238592   .1094058    -3.87   0.000    -.6382906   -.2094277
            div3 |  -.0686194   .1058607    -0.65   0.517    -.2761026    .1388638
            div4 |  -.2621279   .1514647    -1.73   0.084    -.5589933    .0347375
            div5 |  -.2282606   .1846676    -1.24   0.216    -.5902024    .1336813
            div6 |  -.4271466   .1874264    -2.28   0.023    -.7944957   -.0597976
            div7 |  -.3912401   .1890613    -2.07   0.039    -.7617935   -.0206867
            div8 |  -.3773873   .2892246    -1.30   0.192    -.9442572    .1894826
            div9 |   -.324464    .270246    -1.20   0.230    -.8541364    .2052084
           _cons |   6.543923   .9565685     6.84   0.000     4.669083    8.418763
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl div2
            div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            8.477                 2.384
Constant effect |            1.933                 2.511
Dominance       |            0.000                 2.505
Exogeneity      |            2.081                 2.780
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90)  

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            8.477                 2.150
Constant effect |            1.933                 2.184
Dominance       |            0.000                 2.185
Exogeneity      |            2.081                 2.180
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
. * Model 4 *
. 
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_1993", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    5064.85
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9433
                                                  Root MSE        =     .30828

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |    1.08299   .1529358     7.08   0.000     .7832408    1.382738
           l_pop |   .2895093   .1173025     2.47   0.014     .0596007    .5194179
   S_somecollege |   1.110934     .41008     2.71   0.007     .3071922    1.914676
   l_mean_income |   -.818776   .3668535    -2.23   0.026    -1.537796   -.0997564
          S_poor |  -.2570786   .6672814    -0.39   0.700    -1.564926    1.050769
         S_manuf |   1.026007   .4642586     2.21   0.027     .1160765    1.935937
            div2 |  -.1867884   .1128189    -1.66   0.098    -.4079095    .0343327
            div3 |   .0649713   .1251085     0.52   0.604    -.1802368    .3101794
            div4 |  -.1293361   .1447523    -0.89   0.372    -.4130454    .1543732
            div5 |  -.0997413   .1382738    -0.72   0.471    -.3707529    .1712704
            div6 |  -.2336726    .151499    -1.54   0.123    -.5306052      .06326
            div7 |  -.1967181   .1655985    -1.19   0.235    -.5212852    .1278491
            div8 |  -.3935185   .1935199    -2.03   0.042    -.7728106   -.0142264
            div9 |   -.254319   .1811528    -1.40   0.160    -.6093719    .1007339
elevat_range_msa |  -.0475659   .0558235    -0.85   0.394    -.1569779    .0618461
  ruggedness_msa |     7.2235   3.400976     2.12   0.034     .5577098    13.88929
      heating_dd |  -.0127365   .0044775    -2.84   0.004    -.0215122   -.0039608
      cooling_dd |  -.0161507   .0097277    -1.66   0.097    -.0352166    .0029153
          sprawl |  -.0006681   .0033692    -0.20   0.843    -.0072716    .0059355
           _cons |   12.96237   3.471291     3.73   0.000     6.158768    19.76598
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop S_somecoll
> ege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl if year == "_1993",  quantile(10
>  25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(95) = 9740.21
                                                       Prob > chi2   =  0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.517085   .1955832     7.76   0.000     1.133749    1.900421
           l_pop |  -.0066141   .1513173    -0.04   0.965    -.3031905    .2899623
   S_somecollege |   .4894396   .7462359     0.66   0.512     -.973156    1.952035
   l_mean_income |  -.8245809   .7039516    -1.17   0.241    -2.204301    .5551389
          S_poor |  -1.294276   1.386471    -0.93   0.351     -4.01171    1.423158
         S_manuf |   .7631252   .5357809     1.42   0.154     -.286986    1.813237
            div2 |   .3468585   .6202764     0.56   0.576     -.868861    1.562578
            div3 |   .6395421   .6537151     0.98   0.328    -.6417158      1.9208
            div4 |   .3865784   .6308397     0.61   0.540    -.8498448    1.623002
            div5 |   .4841663   .7179698     0.67   0.500    -.9230286    1.891361
            div6 |   .3179993   .6814875     0.47   0.641    -1.017692     1.65369
            div7 |    .281896   .7856928     0.36   0.720    -1.258034    1.821826
            div8 |  -.4721725   .7197385    -0.66   0.512    -1.882834     .938489
            div9 |   .2148649   .9654114     0.22   0.824    -1.677307    2.107036
elevat_range_msa |  -.1737655   .1434899    -1.21   0.226    -.4550005    .1074695
  ruggedness_msa |   22.57963   5.579715     4.05   0.000     11.64358    33.51567
      heating_dd |  -.0155272   .0091035    -1.71   0.088    -.0333699    .0023154
      cooling_dd |  -.0189246   .0164897    -1.15   0.251    -.0512438    .0133945
          sprawl |  -.0025688   .0064329    -0.40   0.690     -.015177    .0100394
           _cons |   13.73167   6.000469     2.29   0.022      1.97097    25.49238
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.249128   .2138632     5.84   0.000      .829964    1.668292
           l_pop |   .1412809    .174704     0.81   0.419    -.2011326    .4836944
   S_somecollege |   .8499395   .7794946     1.09   0.276    -.6778418    2.377721
   l_mean_income |  -.6624757   .7412626    -0.89   0.371    -2.115324    .7903723
          S_poor |  -.3891091   1.188313    -0.33   0.743    -2.718161    1.939942
         S_manuf |   1.134809   .6084787     1.86   0.062    -.0577875    2.327405
            div2 |  -.2631674   .2284326    -1.15   0.249     -.710887    .1845522
            div3 |  -.0661081   .3015752    -0.22   0.826    -.6571846    .5249684
            div4 |  -.3586263   .3191388    -1.12   0.261    -.9841269    .2668744
            div5 |  -.5168847    .376838    -1.37   0.170    -1.255474    .2217042
            div6 |  -.5683827   .4215553    -1.35   0.178    -1.394616    .2578505
            div7 |  -.5552002   .4457967    -1.25   0.213    -1.428946    .3185452
            div8 |  -.9914858   .4371782    -2.27   0.023    -1.848339   -.1346323
            div9 |  -.7941091   .4252957    -1.87   0.062    -1.627673    .0394551
elevat_range_msa |  -.0379087   .0680329    -0.56   0.577    -.1712508    .0954334
  ruggedness_msa |   14.29743   6.366887     2.25   0.025      1.81856     26.7763
      heating_dd |  -.0208182   .0083177    -2.50   0.012    -.0371207   -.0045157
      cooling_dd |  -.0203318   .0144105    -1.41   0.158    -.0485758    .0079123
          sprawl |  -.0021111   .0042175    -0.50   0.617    -.0103772     .006155
           _cons |   12.88392   6.390044     2.02   0.044     .3596598    25.40817
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9015605   .1706636     5.28   0.000      .567066    1.236055
           l_pop |   .3946343   .1352857     2.92   0.004     .1294791    .6597894
   S_somecollege |   .9063833   .4955109     1.83   0.067    -.0648003    1.877567
   l_mean_income |  -.7170143   .5024096    -1.43   0.154    -1.701719    .2676904
          S_poor |   .1210834   1.340501     0.09   0.928     -2.50625    2.748417
         S_manuf |   .0426162   .5695595     0.07   0.940      -1.0737    1.158932
            div2 |  -.1840959   .2107983    -0.87   0.382    -.5972531    .2290613
            div3 |   .1352241   .2212788     0.61   0.541    -.2984743    .5689226
            div4 |   -.086884   .2385172    -0.36   0.716    -.5543691    .3806011
            div5 |  -.0288532   .2295009    -0.13   0.900    -.4786667    .4209603
            div6 |   -.056893   .2320867    -0.25   0.806    -.5117745    .3979885
            div7 |  -.0943861   .2542648    -0.37   0.710    -.5927359    .4039637
            div8 |   -.266246   .3264164    -0.82   0.415    -.9060105    .3735185
            div9 |  -.2893731    .338279    -0.86   0.392    -.9523878    .3736416
elevat_range_msa |  -.0667473   .0963202    -0.69   0.488    -.2555314    .1220368
  ruggedness_msa |   9.015844   3.208135     2.81   0.005     2.728016    15.30367
      heating_dd |  -.0149755   .0059915    -2.50   0.012    -.0267186   -.0032324
      cooling_dd |  -.0284804   .0113799    -2.50   0.012    -.0507845   -.0061762
          sprawl |  -.0013876   .0050492    -0.27   0.783    -.0112839    .0085086
           _cons |    12.2646   4.824881     2.54   0.011     2.808004    21.72119
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .9218933   .2499268     3.69   0.000     .4320458    1.411741
           l_pop |   .3427703   .2048979     1.67   0.094    -.0588223    .7443629
   S_somecollege |   .4101401   1.002848     0.41   0.683    -1.555407    2.375687
   l_mean_income |  -.0855705   .6909171    -0.12   0.901    -1.439743    1.268602
          S_poor |  -.3847452   1.388626    -0.28   0.782    -3.106402    2.336911
         S_manuf |   .0726976   .8868487     0.08   0.935    -1.665494    1.810889
            div2 |   -.353897   .2038216    -1.74   0.083    -.7533799     .045586
            div3 |  -.0200359   .2063367    -0.10   0.923    -.4244485    .3843767
            div4 |  -.1568683   .2820012    -0.56   0.578    -.7095805    .3958438
            div5 |  -.0350947   .3298831    -0.11   0.915    -.6816536    .6114643
            div6 |      -.112   .3235562    -0.35   0.729    -.7461585    .5221585
            div7 |  -.0648023   .3003671    -0.22   0.829    -.6535111    .5239065
            div8 |  -.2156569   .2780218    -0.78   0.438    -.7605696    .3292558
            div9 |  -.1494201   .2739145    -0.55   0.585    -.6862826    .3874424
elevat_range_msa |  -.0458913   .1156665    -0.40   0.692    -.2725934    .1808108
  ruggedness_msa |   5.346717   5.190166     1.03   0.303    -4.825821    15.51925
      heating_dd |  -.0066332   .0086879    -0.76   0.445    -.0236612    .0103947
      cooling_dd |  -.0091649   .0259593    -0.35   0.724    -.0600442    .0417143
          sprawl |   .0005857   .0081416     0.07   0.943    -.0153716     .016543
           _cons |   6.414043   6.706892     0.96   0.339    -6.731223    19.55931
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8581933   .4402142     1.95   0.051    -.0046107    1.720997
           l_pop |   .3816586   .3736721     1.02   0.307    -.3507252    1.114042
   S_somecollege |   1.009336   1.918686     0.53   0.599     -2.75122    4.769892
   l_mean_income |  -.2430595   1.412237    -0.17   0.863    -3.010993    2.524874
          S_poor |  -.6643461   3.168872    -0.21   0.834    -6.875221    5.546529
         S_manuf |   .3225412    1.73486     0.19   0.853    -3.077721    3.722803
            div2 |  -.3890949   .3356221    -1.16   0.246    -1.046902    .2687122
            div3 |    .004015   .3784194     0.01   0.992    -.7376733    .7457033
            div4 |  -.1113379   .5239555    -0.21   0.832    -1.138272     .915596
            div5 |  -.2464904    .565364    -0.44   0.663    -1.354584    .8616026
            div6 |  -.3246747   .5029206    -0.65   0.519    -1.310381    .6610317
            div7 |  -.2486332   .5802732    -0.43   0.668    -1.385948    .8886814
            div8 |  -.1959427   .7055186    -0.28   0.781    -1.578734    1.186848
            div9 |    -.30396   .4910974    -0.62   0.536    -1.266493    .6585732
elevat_range_msa |  -.0200894   .2790703    -0.07   0.943    -.5670572    .5268784
  ruggedness_msa |   3.434994   10.07692     0.34   0.733     -16.3154    23.18539
      heating_dd |   -.008672   .0164406    -0.53   0.598     -.040895     .023551
      cooling_dd |  -.0024825   .0345758    -0.07   0.943    -.0702498    .0652849
          sprawl |   .0058159   .0084608     0.69   0.492     -.010767    .0223987
           _cons |   7.649119   14.31832     0.53   0.593    -20.41427     35.7125
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            5.948                 2.192
Constant effect |            2.655                 2.460
Dominance       |            0.000                 2.331
Exogeneity      |            2.489                 2.351
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            5.948                 1.881
Constant effect |            2.655                 2.271
Dominance       |            0.000                 2.071
Exogeneity      |            2.489                 1.975
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
.                 
. * Model 5 * 
. 
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_1993", robust 

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(26)   =    5359.22
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9431
                                                  Root MSE        =     .30888

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   1.144559   .1685979     6.79   0.000     .8141136    1.475005
           l_pop |   .0261896   .3319801     0.08   0.937    -.6244794    .6768586
         l_pop80 |   .9193203   .5807054     1.58   0.113    -.2188414    2.057482
         l_pop70 |  -.0027197   .6152809    -0.00   0.996    -1.208648    1.203209
         l_pop60 |  -.8305019   .4989422    -1.66   0.096    -1.808411    .1474068
         l_pop50 |   .0084253   .3336952     0.03   0.980    -.6456052    .6624559
         l_pop40 |  -.0074528   .3626622    -0.02   0.984    -.7182576    .7033521
         l_pop30 |   .1392354   .2835559     0.49   0.623     -.416524    .6949947
         l_pop20 |   -.003821   .1389434    -0.03   0.978    -.2761451    .2685031
   S_somecollege |    .362046   .5470456     0.66   0.508    -.7101436    1.434236
   l_mean_income |  -.7038179   .3746285    -1.88   0.060    -1.438076    .0304405
          S_poor |   .0951441   .6919842     0.14   0.891     -1.26112    1.451408
         S_manuf |   .9234655   .4445528     2.08   0.038      .052158    1.794773
            div2 |  -.1975844    .116514    -1.70   0.090    -.4259477    .0307789
            div3 |   .0238583   .1334998     0.18   0.858    -.2377966    .2855132
            div4 |  -.1170409   .1453341    -0.81   0.421    -.4018906    .1678088
            div5 |  -.1569818    .148658    -1.06   0.291    -.4483462    .1343826
            div6 |  -.2779283   .1597759    -1.74   0.082    -.5910832    .0352267
            div7 |  -.1831058   .1754858    -1.04   0.297    -.5270517      .16084
            div8 |  -.4765189   .2292798    -2.08   0.038    -.9258991   -.0271387
            div9 |  -.2611827   .2213351    -1.18   0.238    -.6949916    .1726262
elevat_range_msa |  -.0625092   .0562626    -1.11   0.267    -.1727818    .0477635
  ruggedness_msa |   4.858655   3.573066     1.36   0.174    -2.144426    11.86174
      heating_dd |  -.0151378   .0047742    -3.17   0.002     -.024495   -.0057805
      cooling_dd |  -.0288267   .0107574    -2.68   0.007    -.0499109   -.0077425
          sprawl |   -.001524   .0036019    -0.42   0.672    -.0085836    .0055356
           _cons |    12.4732   3.654967     3.41   0.001     5.309591     19.6368
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop l_pop80 l_
> pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_
> manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heati
> ng_dd cooling_dd sprawl if year == "_1993",  quantile(10 25 50 75 90) bound(0 6) vc
> e(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                               Number of obs  =      228
Estimator: Inverse quantile regression               Wald chi2(130) = 11398.53
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.630916   .4661019     3.50   0.000     .7173726    2.544459
           l_pop |  -.1510299   1.030637    -0.15   0.883    -2.171041    1.868981
         l_pop80 |   .7048384   1.947501     0.36   0.717    -3.112193     4.52187
         l_pop70 |   .7197537   1.007296     0.71   0.475     -1.25451    2.694018
         l_pop60 |  -1.549975   1.893091    -0.82   0.413    -5.260366    2.160416
         l_pop50 |  -.3178107   .8466898    -0.38   0.707    -1.977292    1.341671
         l_pop40 |   .6491415   .7383695     0.88   0.379    -.7980361    2.096319
         l_pop30 |   .0492864   .8424113     0.06   0.953    -1.601809    1.700382
         l_pop20 |    -.26193   .6985027    -0.37   0.708     -1.63097     1.10711
   S_somecollege |  -.9980582   1.862853    -0.54   0.592    -4.649183    2.653067
   l_mean_income |   .2517304   1.807931     0.14   0.889     -3.29175    3.795211
          S_poor |   1.672294   2.868188     0.58   0.560    -3.949251    7.293839
         S_manuf |   1.394068   1.465963     0.95   0.342    -1.479167    4.267303
            div2 |   .3770885   .7433597     0.51   0.612     -1.07987    1.834047
            div3 |   .4207616   .8080643     0.52   0.603    -1.163015    2.004538
            div4 |   .4315707   .7656874     0.56   0.573    -1.069149     1.93229
            div5 |   .2214567   .8497899     0.26   0.794    -1.444101    1.887014
            div6 |   .1923755   .8885918     0.22   0.829    -1.549232    1.933983
            div7 |   .1414995   1.054536     0.13   0.893    -1.925353    2.208352
            div8 |  -.4437197   .9396521    -0.47   0.637    -2.285404    1.397965
            div9 |   .3279337   1.223788     0.27   0.789    -2.070648    2.726515
elevat_range_msa |   -.220749   .2413507    -0.91   0.360    -.6937877    .2522898
  ruggedness_msa |   11.70365   16.27254     0.72   0.472    -20.18994    43.59725
      heating_dd |  -.0221818   .0141639    -1.57   0.117    -.0499426    .0055789
      cooling_dd |  -.0466874   .0365889    -1.28   0.202    -.1184003    .0250255
          sprawl |  -.0065063   .0115508    -0.56   0.573    -.0291455    .0161328
           _cons |   4.901487   14.47981     0.34   0.735    -23.47841    33.28138
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.358383   .3045121     4.46   0.000     .7615501    1.955216
           l_pop |  -.1734642   .4021292    -0.43   0.666    -.9616231    .6146946
         l_pop80 |   1.247466   .6169358     2.02   0.043     .0382942    2.456638
         l_pop70 |  -.2051358   .5760345    -0.36   0.722    -1.334143    .9238711
         l_pop60 |  -.7530905   .6789322    -1.11   0.267    -2.083773    .5775922
         l_pop50 |  -.4083624   .4460539    -0.92   0.360    -1.282612    .4658873
         l_pop40 |   .4415632   .5514117     0.80   0.423     -.639184     1.52231
         l_pop30 |  -.0801673   .3341721    -0.24   0.810    -.7351326    .5747979
         l_pop20 |   .0363696   .2816522     0.13   0.897    -.5156586    .5883979
   S_somecollege |   .2349298   .9399204     0.25   0.803     -1.60728     2.07714
   l_mean_income |  -.8685925    .464003    -1.87   0.061    -1.778022    .0408368
          S_poor |   .0071197   1.144994     0.01   0.995    -2.237028    2.251267
         S_manuf |   1.230993   .6619298     1.86   0.063    -.0663654    2.528352
            div2 |  -.2824704   .2140118    -1.32   0.187    -.7019258    .1369851
            div3 |  -.1120413   .2512115    -0.45   0.656    -.6044068    .3803243
            div4 |  -.2980034   .2629101    -1.13   0.257    -.8132978    .2172909
            div5 |  -.4234423   .2504502    -1.69   0.091    -.9143157    .0674311
            div6 |    -.47278   .2727196    -1.73   0.083    -1.007301    .0617407
            div7 |  -.3006071     .28622    -1.05   0.294    -.8615881    .2603739
            div8 |  -.9563415   .4297726    -2.23   0.026     -1.79868   -.1140028
            div9 |  -.1965523   .2983206    -0.66   0.510      -.78125    .3881453
elevat_range_msa |  -.1484194   .0730975    -2.03   0.042    -.2916878   -.0051509
  ruggedness_msa |   8.978615   3.883858     2.31   0.021     1.366394    16.59084
      heating_dd |  -.0205317   .0056002    -3.67   0.000    -.0315079   -.0095556
      cooling_dd |  -.0379182   .0149973    -2.53   0.011    -.0673123    -.008524
          sprawl |  -.0017137   .0042053    -0.41   0.684    -.0099559    .0065285
           _cons |   14.95906   4.325071     3.46   0.001     6.482077    23.43605
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9212842   .1919614     4.80   0.000     .5450468    1.297522
           l_pop |   .3508928   .4547528     0.77   0.440    -.5404062    1.242192
         l_pop80 |   .3507196   .7745332     0.45   0.651    -1.167338    1.868777
         l_pop70 |   .3825133   .7251571     0.53   0.598    -1.038769    1.803795
         l_pop60 |  -.9142768   .7908017    -1.16   0.248     -2.46422     .635666
         l_pop50 |   .1454441   .4500621     0.32   0.747    -.7366614     1.02755
         l_pop40 |   .0695809   .5211247     0.13   0.894    -.9518049    1.090967
         l_pop30 |  -.0115417   .5539154    -0.02   0.983    -1.097196    1.074113
         l_pop20 |   .0143326   .2004721     0.07   0.943    -.3785855    .4072507
   S_somecollege |   .2929836   .7357539     0.40   0.690    -1.149068    1.735035
   l_mean_income |  -.7866552    .542779    -1.45   0.147    -1.850482    .2771721
          S_poor |  -.1426453   1.217394    -0.12   0.907    -2.528694    2.243403
         S_manuf |   .1207983   .6241614     0.19   0.847    -1.102535    1.344132
            div2 |  -.2119756    .284995    -0.74   0.457    -.7705555    .3466043
            div3 |   .1057227   .2879556     0.37   0.714    -.4586599    .6701052
            div4 |  -.0305192   .3103104    -0.10   0.922    -.6387163     .577678
            div5 |  -.1638701   .3229575    -0.51   0.612    -.7968552     .469115
            div6 |  -.1553771   .3176929    -0.49   0.625    -.7780437    .4672895
            div7 |  -.1580221   .3075226    -0.51   0.607    -.7607554    .4447113
            div8 |   -.459997   .5008445    -0.92   0.358    -1.441634    .5216401
            div9 |  -.5059983    .431751    -1.17   0.241    -1.352215    .3402181
elevat_range_msa |  -.0001559   .0831479    -0.00   0.999    -.1631229     .162811
  ruggedness_msa |   6.818379   4.123833     1.65   0.098    -1.264184    14.90094
      heating_dd |  -.0194819   .0057394    -3.39   0.001    -.0307309   -.0082329
      cooling_dd |  -.0384063   .0156993    -2.45   0.014    -.0691763   -.0076363
          sprawl |  -.0016447     .00617    -0.27   0.790    -.0137377    .0104483
           _cons |   13.52241   5.603727     2.41   0.016     2.539307    24.50551
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   1.050654   .2251302     4.67   0.000     .6094069    1.491901
           l_pop |  -.1339989   .4819588    -0.28   0.781    -1.078621    .8106229
         l_pop80 |    1.30869   1.085925     1.21   0.228    -.8196849    3.437064
         l_pop70 |  -.5839342   1.296327    -0.45   0.652    -3.124688     1.95682
         l_pop60 |  -.6321042   .8819823    -0.72   0.474    -2.360758    1.096549
         l_pop50 |   .0586909   .6562095     0.09   0.929    -1.227456    1.344838
         l_pop40 |   .2170271   .7587606     0.29   0.775    -1.270116    1.704171
         l_pop30 |  -.0681935   .5796129    -0.12   0.906    -1.204214    1.067827
         l_pop20 |   .0518259   .1900383     0.27   0.785    -.3206423    .4242941
   S_somecollege |   .2443774   1.223293     0.20   0.842    -2.153233    2.641988
   l_mean_income |  -.1342268    .720486    -0.19   0.852    -1.546353      1.2779
          S_poor |   .1209418    1.03605     0.12   0.907    -1.909679    2.151563
         S_manuf |   .6132536   .5816316     1.05   0.292    -.5267233    1.753231
            div2 |  -.3354831    .201727    -1.66   0.096    -.7308607    .0598944
            div3 |  -.1012348   .2455254    -0.41   0.680    -.5824558    .3799861
            div4 |  -.2834732   .3039717    -0.93   0.351    -.8792469    .3123004
            div5 |  -.1419446   .4134077    -0.34   0.731    -.9522087    .6683196
            div6 |  -.3003969      .4058    -0.74   0.459     -1.09575    .4949566
            div7 |  -.2893571   .4542117    -0.64   0.524    -1.179596    .6008815
            div8 |  -.4474811   .8871723    -0.50   0.614    -2.186307    1.291345
            div9 |  -.3434315   .7724678    -0.44   0.657     -1.85744    1.170577
elevat_range_msa |  -.0568305    .141507    -0.40   0.688    -.3341791    .2205181
  ruggedness_msa |   5.651525   7.813829     0.72   0.470    -9.663298    20.96635
      heating_dd |  -.0132014   .0117297    -1.13   0.260    -.0361913    .0097884
      cooling_dd |   -.024459   .0240198    -1.02   0.309     -.071537     .022619
          sprawl |  -.0069902   .0066065    -1.06   0.290    -.0199386    .0059583
           _cons |   8.160632   6.671233     1.22   0.221    -4.914744    21.23601
-----------------+----------------------------------------------------------------
q90              |
            l_ln |    .811415   .4885663     1.66   0.097    -.1461573    1.768987
           l_pop |   .6088941   1.204305     0.51   0.613    -1.751501     2.96929
         l_pop80 |  -.1691456   2.663819    -0.06   0.949    -5.390136    5.051845
         l_pop70 |   .5510749   3.251887     0.17   0.865    -5.822506    6.924655
         l_pop60 |  -.9959574   2.692192    -0.37   0.711    -6.272557    4.280642
         l_pop50 |   .2315078   3.604868     0.06   0.949    -6.833904    7.296919
         l_pop40 |  -.0201909   2.056885    -0.01   0.992    -4.051611    4.011229
         l_pop30 |   .3712568   1.188347     0.31   0.755     -1.95786    2.700374
         l_pop20 |  -.1740501   .5936416    -0.29   0.769    -1.337566    .9894661
   S_somecollege |   .7140074   2.662765     0.27   0.789    -4.504917    5.932932
   l_mean_income |  -.0680974   1.837397    -0.04   0.970     -3.66933    3.533135
          S_poor |  -1.073323   5.331734    -0.20   0.840    -11.52333    9.376683
         S_manuf |  -.0780018   1.724407    -0.05   0.964    -3.457778    3.301775
            div2 |  -.4037776   .3844908    -1.05   0.294    -1.157366    .3498105
            div3 |  -.0328689   .4508197    -0.07   0.942    -.9164593    .8507215
            div4 |  -.2264903   .6335407    -0.36   0.721    -1.468207    1.015227
            div5 |  -.1973772   .6262878    -0.32   0.753    -1.424879    1.030124
            div6 |  -.2096136   .5198717    -0.40   0.687    -1.228543    .8093162
            div7 |   -.163384   .6688055    -0.24   0.807    -1.474219    1.147451
            div8 |  -.1136985   .9736624    -0.12   0.907    -2.022042    1.794645
            div9 |  -.1762146   .7810234    -0.23   0.821    -1.706992    1.354563
elevat_range_msa |  -.0466904   .2179735    -0.21   0.830    -.4739105    .3805298
  ruggedness_msa |   3.888099   15.66206     0.25   0.804    -26.80898    34.58517
      heating_dd |  -.0045145   .0185625    -0.24   0.808    -.0408963    .0318673
      cooling_dd |   .0041063   .0611273     0.07   0.946    -.1157009    .1239136
          sprawl |   .0059624   .0099095     0.60   0.547    -.0134598    .0253845
           _cons |    5.90949      18.31     0.32   0.747    -29.97746    41.79644
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            4.799                 2.269
Constant effect |            1.851                 2.214
Dominance       |            0.000                 2.545
Exogeneity      |            1.859                 2.201
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            4.799                 2.003
Constant effect |            1.851                 1.754
Dominance       |            0.000                 2.221
Exogeneity      |            1.859                 1.968
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
.                 
.                 
.                 
. *** Decade 3 ('00s) *** 
. 
. 
. use "Duranton_Turner_AER_2010.dta", clear

. 
. 
.         * Selection of observations
.         drop if l_ln_km_IH_83==0
(47 observations deleted)

. 
.         rename sprawl_1992 sprawl_1993

.         rename sprawl_1976 sprawl_1983

.         gen sprawl_2003 = sprawl_1993

. 
.         *rescale so display is better
.         replace elevat_range_msa = elevat_range_msa/1000 
variable elevat_range_msa was int now float
(228 real changes made)

.         replace ruggedness_msa = ruggedness_msa/1000
(228 real changes made)

.         replace heating_dd = heating_dd/100
(228 real changes made)

.         replace cooling_dd = cooling_dd/100
(228 real changes made)

. 
.       rename S_somecollege_80 S_somecollege_1983

.       rename S_somecollege_00 S_somecollege_2003

.       rename S_somecollege_90 S_somecollege_1993

.       rename S_poor_80 S_poor_1983

.       rename S_poor_90 S_poor_1993

.       rename S_poor_00 S_poor_2003

.       rename l_mean_income_80 l_mean_income_1983

.       rename l_mean_income_90 l_mean_income_1993

.       rename l_mean_income_00 l_mean_income_2003

.       rename S_manuf83 S_manuf_1983

.       rename S_manuf93 S_manuf_1993

.       rename S_manuf03 S_manuf_2003

.       rename S_truck83 S_truck_1983

.       rename S_truck93 S_truck_1993

.       rename S_truck03 S_truck_2003

.       gen l_pop_1983 = l_pop80 

.       gen l_pop_1993 = l_pop90 

.       gen l_pop_2003 = l_pop00 

. 
.       rename l_max_84bus l_bus_1983 

.       rename l_max_94bus l_bus_1993 

.       rename l_max_04bus l_bus_2003 

.       rename l_transit84 l_transit_1983

.       rename l_transit94 l_transit_1993 

.       rename l_transit04 l_transit_2003

. 
.       rename l_ln_km_IHU_83 l_ln_km_IHU_1983

.       rename l_ln_km_IHU_93 l_ln_km_IHU_1993

.       rename l_ln_km_IHU_03 l_ln_km_IHU_2003

.       rename l_ln_km_IH_83 l_ln_km_IH_1983

.       rename l_ln_km_IH_93 l_ln_km_IH_1993

.       rename l_ln_km_IH_03 l_ln_km_IH_2003

.       rename l_ln_km_MRU_83 l_ln_km_MRU_1983

.       rename l_ln_km_MRU_93 l_ln_km_MRU_1993

.       rename l_ln_km_MRU_03 l_ln_km_MRU_2003

.       
.       rename l_vmt_IHU_83 l_vmt_IHU_1983

.       rename l_vmt_IHU_93 l_vmt_IHU_1993

.       rename l_vmt_IHU_03 l_vmt_IHU_2003

.       rename l_vmt_IH_83 l_vmt_IH_1983

.       rename l_vmt_IH_93 l_vmt_IH_1993

.       rename l_vmt_IH_03 l_vmt_IH_2003

.       rename l_vmt_MRU_83 l_vmt_MRU_1983

.       rename l_vmt_MRU_93 l_vmt_MRU_1993

.       rename l_vmt_MRU_03 l_vmt_MRU_2003

. 
. 
. 
.         reshape long l_ln_km_IH  l_ln_km_IHU  l_ln_km_IHNU  l_ln_km_MRU l_vmt_IH l_
> vmt_IHU  l_vmt_IHNU  l_vmt_MRU l_bus l_transit sprawl S_somecollege l_mean_income S
> _poor S_manuf S_truck l_pop, i(msa ) j(year _1983 _1993 _2003)
(variable l_ln_km_IHNU_1983 not found)
(variable l_vmt_IHNU_1983 not found)
(variable l_ln_km_IHNU_1993 not found)
(variable l_vmt_IHNU_1993 not found)
(variable l_ln_km_IHNU_2003 not found)
(variable l_vmt_IHNU_2003 not found)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations              228   ->   684         
Number of variables                 217   ->   190         
j variable (3 values)                     ->   year
xij variables:
l_ln_km_IH_1983 l_ln_km_IH_1993 l_ln_km_IH_2003->l_ln_km_IH
l_ln_km_IHU_1983 l_ln_km_IHU_1993 l_ln_km_IHU_2003->l_ln_km_IHU
l_ln_km_IHNU_1983 l_ln_km_IHNU_1993 l_ln_km_IHNU_2003->l_ln_km_IHNU
l_ln_km_MRU_1983 l_ln_km_MRU_1993 l_ln_km_MRU_2003->l_ln_km_MRU
l_vmt_IH_1983 l_vmt_IH_1993 l_vmt_IH_2003 ->   l_vmt_IH
l_vmt_IHU_1983 l_vmt_IHU_1993 l_vmt_IHU_2003-> l_vmt_IHU
l_vmt_IHNU_1983 l_vmt_IHNU_1993 l_vmt_IHNU_2003->l_vmt_IHNU
l_vmt_MRU_1983 l_vmt_MRU_1993 l_vmt_MRU_2003-> l_vmt_MRU
       l_bus_1983 l_bus_1993 l_bus_2003   ->   l_bus
l_transit_1983 l_transit_1993 l_transit_2003-> l_transit
    sprawl_1983 sprawl_1993 sprawl_2003   ->   sprawl
S_somecollege_1983 S_somecollege_1993 S_somecollege_2003->S_somecollege
l_mean_income_1983 l_mean_income_1993 l_mean_income_2003->l_mean_income
    S_poor_1983 S_poor_1993 S_poor_2003   ->   S_poor
 S_manuf_1983 S_manuf_1993 S_manuf_2003   ->   S_manuf
 S_truck_1983 S_truck_1993 S_truck_2003   ->   S_truck
       l_pop_1983 l_pop_1993 l_pop_2003   ->   l_pop
-----------------------------------------------------------------------------

. 
.         local geography  "elevat_range_msa ruggedness_msa heating_dd cooling_dd spr
> awl"

.         local demographics "S_somecollege l_mean_income S_poor S_manuf"  

.         local census_div "div1 div2 div3 div4 div5 div6 div7 div8 div9"

.         local population "l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20" 
>  

. 
. 
.         
. 
. 
. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 keep if year == "_2003" 
(456 observations deleted)

. 
.                 local Inst "l_rail1898 l_hwy1947"

.                 capture drop l_vmt l_ln

.                 gen l_vmt = l_vmt_IH  

.                 gen l_ln  = l_ln_km_IH 

. 
.                 
. 
. * Model 1 *
. 
.                 ivregress liml l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_200
> 3", robust       

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(1)    =     705.86
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.8759
                                                  Root MSE        =     .45051

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   1.258871   .0473829    26.57   0.000     1.166002    1.351739
       _cons |   7.321904   .3226186    22.70   0.000     6.689584    7.954225
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) if year == "_200
> 3",  quantile(10 25 50 75 90) vce(robust)   

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(5)  = 2195.08
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |    1.44321   .0467462    30.87   0.000     1.351589    1.534831
       _cons |   5.582077   .3640178    15.33   0.000     4.868615    6.295539
-------------+----------------------------------------------------------------
q25          |
        l_ln |   1.375839   .0456127    30.16   0.000      1.28644    1.465239
       _cons |   6.334606   .3366587    18.82   0.000     5.674767    6.994445
-------------+----------------------------------------------------------------
q50          |
        l_ln |   1.223447   .0353139    34.64   0.000     1.154233    1.292661
       _cons |   7.653621   .2489444    30.74   0.000     7.165699    8.141543
-------------+----------------------------------------------------------------
q75          |
        l_ln |   1.165678   .0309338    37.68   0.000     1.105049    1.226307
       _cons |   8.216106   .2152887    38.16   0.000     7.794148    8.638064
-------------+----------------------------------------------------------------
q90          |
        l_ln |   1.141359   .0384435    29.69   0.000     1.066012    1.216707
       _cons |   8.578956   .2774747    30.92   0.000     8.035115    9.122796
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           34.645                 2.167
Constant effect |            4.238                 2.001
Dominance       |            0.000                 2.097
Exogeneity      |            2.271                 2.390
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           34.645                 2.022
Constant effect |            4.238                 1.721
Dominance       |            0.000                 2.046
Exogeneity      |            2.271                 1.983
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. * Model 2 *
. 
.                 ivregress liml l_vmt l_pop (l_ln = l_rail1898 l_hwy1947) if year ==
>  "_2003", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(2)    =    2625.85
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9388
                                                  Root MSE        =     .31648

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        l_ln |   .8165659   .1143325     7.14   0.000     .5924783    1.040654
       l_pop |   .4541329   .0859888     5.28   0.000     .2855981    .6226678
       _cons |   4.376733   .4235033    10.33   0.000     3.546681    5.206784
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                   
.                  ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop if year =
> = "_2003",  quantile(10 25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(10) = 4497.12
                                                       Prob > chi2   =  0.0000

------------------------------------------------------------------------------
             |               Robust
       l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
q10          |
        l_ln |   .9543819   .1235594     7.72   0.000       .71221    1.196554
       l_pop |    .425046   .0854944     4.97   0.000     .2574799     .592612
       _cons |   3.310795   .3798133     8.72   0.000     2.566375    4.055216
-------------+----------------------------------------------------------------
q25          |
        l_ln |   .9483964   .2142687     4.43   0.000     .5284376    1.368355
       l_pop |   .3507646   .1472006     2.38   0.017     .0622567    .6392724
       _cons |   4.464549   .6244271     7.15   0.000     3.240694    5.688404
-------------+----------------------------------------------------------------
q50          |
        l_ln |   .7187256   .1285175     5.59   0.000      .466836    .9706153
       l_pop |    .494399   .1019159     4.85   0.000     .2946476    .6941504
       _cons |   4.503519   .5305912     8.49   0.000      3.46358    5.543459
-------------+----------------------------------------------------------------
q75          |
        l_ln |   .6794702   .1136354     5.98   0.000     .4567488    .9021915
       l_pop |   .5101247   .0969627     5.26   0.000     .3200813    .7001682
       _cons |   4.770558   .5670945     8.41   0.000     3.659073    5.882043
-------------+----------------------------------------------------------------
q90          |
        l_ln |   .7096856    .126251     5.62   0.000     .4622383     .957133
       l_pop |   .4766361   .1108329     4.30   0.000     .2594077    .6938645
       _cons |   5.210944   .7096411     7.34   0.000     3.820073    6.601815
------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_rail1898 l_hwy1947

.                  
.                  estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |           10.978                 2.338
Constant effect |            1.636                 2.232
Dominance       |            0.000                 2.396
Exogeneity      |            1.429                 2.194
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                  estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |           10.978                 2.212
Constant effect |            1.636                 2.016
Dominance       |            0.000                 2.244
Exogeneity      |            1.429                 2.011
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

. 
. 
. * Model 3 *
. 
.                 ivregress liml l_vmt l_pop elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 div8 div9 (l_ln = l_rail1898 l_h
> wy1947) if year == "_2003", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(15)   =    4991.34
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9507
                                                  Root MSE        =     .28406

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9286222   .1276992     7.27   0.000     .6783363    1.178908
           l_pop |   .3455688   .1029213     3.36   0.001     .1438468    .5472907
elevat_range_msa |  -.0082302   .0553744    -0.15   0.882     -.116762    .1003016
  ruggedness_msa |   6.035588   3.324306     1.82   0.069    -.4799323    12.55111
      heating_dd |  -.0134167   .0040487    -3.31   0.001    -.0213519   -.0054814
      cooling_dd |  -.0202258   .0083641    -2.42   0.016    -.0366192   -.0038324
          sprawl |   .0004941   .0027953     0.18   0.860    -.0049845    .0059727
            div2 |  -.1900602   .0920864    -2.06   0.039    -.3705463   -.0095741
            div3 |   .0086584   .0985216     0.09   0.930    -.1844405    .2017573
            div4 |  -.0522848   .1212526    -0.43   0.666    -.2899356     .185366
            div5 |  -.0589823   .1295412    -0.46   0.649    -.3128785    .1949138
            div6 |  -.1243033   .1346421    -0.92   0.356    -.3881969    .1395904
            div7 |  -.2006758   .1455615    -1.38   0.168    -.4859711    .0846196
            div8 |  -.4006379   .1732224    -2.31   0.021    -.7401475   -.0611282
            div9 |  -.3898768   .1762187    -2.21   0.027     -.735259   -.0444946
           _cons |   6.007039   .7711651     7.79   0.000     4.495583    7.518495
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl
            div2 div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl div2 div3 div4 div5 div6 div7 di
> v8 div9 if year == "_2003",  quantile(10 25 50 75 90) vce(robust)         

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                 Number of obs =     228
Estimator: Inverse quantile regression                 Wald chi2(75) = 9031.39
                                                       Prob > chi2   =  0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.279303   .1365435     9.37   0.000     1.011683    1.546924
           l_pop |   .1435216   .1024581     1.40   0.161    -.0572925    .3443358
elevat_range_msa |  -.1535081   .1251581    -1.23   0.220    -.3988135    .0917972
  ruggedness_msa |   12.91577   4.453928     2.90   0.004     4.186229    21.64531
      heating_dd |  -.0196709   .0080489    -2.44   0.015    -.0354465   -.0038952
      cooling_dd |  -.0282489   .0148464    -1.90   0.057    -.0573473    .0008494
          sprawl |   .0048217   .0040176     1.20   0.230    -.0030527     .012696
            div2 |   .1644603   .2706047     0.61   0.543    -.3659151    .6948358
            div3 |   .1830007   .2722576     0.67   0.501    -.3506143    .7166157
            div4 |   .0852792   .2669725     0.32   0.749    -.4379772    .6085357
            div5 |   .1343644   .3297996     0.41   0.684     -.512031    .7807599
            div6 |   .0639469   .3271326     0.20   0.845    -.5772213     .705115
            div7 |   .0350907   .3330219     0.11   0.916    -.6176203    .6878017
            div8 |  -.3751084   .3167532    -1.18   0.236    -.9959333    .2457165
            div9 |  -.0219195   .4430528    -0.05   0.961    -.8902871    .8464481
           _cons |   6.040931   1.190562     5.07   0.000     3.707472     8.37439
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.149566   .2111691     5.44   0.000     .7356826     1.56345
           l_pop |   .1583247   .1675554     0.94   0.345    -.1700778    .4867273
elevat_range_msa |   .0869668   .0550519     1.58   0.114     -.020933    .1948666
  ruggedness_msa |   7.815116   4.047022     1.93   0.053    -.1169018    15.74713
      heating_dd |  -.0180888   .0053976    -3.35   0.001    -.0286679   -.0075097
      cooling_dd |  -.0229634   .0127224    -1.80   0.071    -.0478989     .001972
          sprawl |  -.0036104   .0034685    -1.04   0.298    -.0104086    .0031878
            div2 |  -.2117501   .1935605    -1.09   0.274    -.5911217    .1676215
            div3 |  -.0115214    .198127    -0.06   0.954    -.3998432    .3768005
            div4 |  -.2159353   .2400963    -0.90   0.368    -.6865153    .2546447
            div5 |  -.2063908   .2470257    -0.84   0.403    -.6905522    .2777706
            div6 |  -.2629496   .2477928    -1.06   0.289    -.7486146    .2227154
            div7 |  -.4167551   .2559971    -1.63   0.104    -.9185001      .08499
            div8 |   -.973827    .353119    -2.76   0.006    -1.665927   -.2817266
            div9 |  -.7356348   .2560469    -2.87   0.004    -1.237478   -.2337922
           _cons |   7.354852   1.204808     6.10   0.000     4.993472    9.716232
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .8118095   .1055328     7.69   0.000      .604969     1.01865
           l_pop |   .3972103   .0930359     4.27   0.000     .2148633    .5795573
elevat_range_msa |   .0270585   .0829554     0.33   0.744    -.1355311    .1896482
  ruggedness_msa |   5.352326   3.354458     1.60   0.111    -1.222291    11.92694
      heating_dd |  -.0141171   .0055618    -2.54   0.011     -.025018   -.0032162
      cooling_dd |  -.0190875   .0115902    -1.65   0.100    -.0418039    .0036289
          sprawl |   .0001067    .003498     0.03   0.976    -.0067493    .0069627
            div2 |  -.2595336   .3457184    -0.75   0.453    -.9371292     .418062
            div3 |   .0603711   .3443206     0.18   0.861    -.6144848     .735227
            div4 |  -.0573141   .3479786    -0.16   0.869    -.7393396    .6247114
            div5 |  -.0796942   .3611642    -0.22   0.825    -.7875629    .6281746
            div6 |  -.1241529    .365587    -0.34   0.734    -.8406903    .5923845
            div7 |  -.1947416   .3622176    -0.54   0.591     -.904675    .5151918
            div8 |  -.4343137   .4224063    -1.03   0.304    -1.262215    .3935874
            div9 |   -.422724   .4091801    -1.03   0.302    -1.224702    .3792542
           _cons |   6.175515   .9430974     6.55   0.000     4.327078    8.023952
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .7262184   .1251685     5.80   0.000     .4808925    .9715442
           l_pop |   .4562183   .1008216     4.53   0.000     .2586116     .653825
elevat_range_msa |  -.0531287   .0728942    -0.73   0.466    -.1959988    .0897414
  ruggedness_msa |   4.334371   3.816795     1.14   0.256    -3.146409    11.81515
      heating_dd |   -.008397   .0050158    -1.67   0.094    -.0182278    .0014337
      cooling_dd |   -.016208   .0122831    -1.32   0.187    -.0402825    .0078664
          sprawl |  -.0008656   .0036607    -0.24   0.813    -.0080406    .0063093
            div2 |  -.3008838   .1002454    -3.00   0.003    -.4973613   -.1044064
            div3 |  -.0948979   .0966528    -0.98   0.326    -.2843339    .0945381
            div4 |  -.1883749   .1269478    -1.48   0.138     -.437188    .0604383
            div5 |  -.0588958   .1242728    -0.47   0.636     -.302466    .1846744
            div6 |   -.062931   .1183328    -0.53   0.595    -.2948591    .1689971
            div7 |  -.1729824   .1304532    -1.33   0.185     -.428666    .0827012
            div8 |  -.2828825   .1822049    -1.55   0.121    -.6399975    .0742325
            div9 |  -.2289651   .1665857    -1.37   0.169    -.5554672    .0975369
           _cons |   5.933243   .6773655     8.76   0.000     4.605631    7.260855
-----------------+----------------------------------------------------------------
q90              |
            l_ln |    .605923   .1582908     3.83   0.000     .2956788    .9161673
           l_pop |   .5845419   .1336092     4.38   0.000     .3226725    .8464112
elevat_range_msa |  -.1530115   .1020332    -1.50   0.134    -.3529929    .0469699
  ruggedness_msa |   6.294097   4.791281     1.31   0.189    -3.096642    15.68484
      heating_dd |   -.002906    .006846    -0.42   0.671    -.0163239     .010512
      cooling_dd |  -.0092035   .0153904    -0.60   0.550    -.0393682    .0209612
          sprawl |    .003504   .0042075     0.83   0.405    -.0047425    .0117505
            div2 |   -.218434   .1428507    -1.53   0.126    -.4984163    .0615483
            div3 |   .0014773   .1164865     0.01   0.990     -.226832    .2297866
            div4 |  -.0589002   .1704953    -0.35   0.730     -.393065    .2752645
            div5 |   .0923965   .2139967     0.43   0.666    -.3270294    .5118224
            div6 |   .0576835   .1638781     0.35   0.725    -.2635117    .3788787
            div7 |   .1006866   .2257147     0.45   0.656     -.341706    .5430793
            div8 |   .1315331   .2609396     0.50   0.614    -.3798992    .6429653
            div9 |    .086362   .3001935     0.29   0.774    -.5020064    .6747304
           _cons |   4.554052   .9988355     4.56   0.000     2.596371    6.511734
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl div2
            div3 div4 div5 div6 div7 div8 div9 l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            9.668                 2.511
Constant effect |            3.555                 2.771
Dominance       |            0.000                 2.672
Exogeneity      |            1.976                 2.511
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90)  

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            9.668                 2.260
Constant effect |            3.555                 2.415
Dominance       |            0.000                 2.484
Exogeneity      |            1.976                 2.156
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.         
. 
. * Model 4 *
. 
.                 ivregress liml l_vmt l_pop S_somecollege l_mean_income S_poor S_man
> uf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_
> dd cooling_dd sprawl (l_ln = l_rail1898 l_hwy1947) if year == "_2003", robust      

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(19)   =    5830.03
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9556
                                                  Root MSE        =     .26967

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9233655   .1290701     7.15   0.000     .6703929    1.176338
           l_pop |   .3658209   .1015085     3.60   0.000     .1668679    .5647739
   S_somecollege |   1.076834    .309514     3.48   0.001     .4701978     1.68347
   l_mean_income |  -.7171055   .2887513    -2.48   0.013    -1.283048   -.1511634
          S_poor |  -1.461358   .7057516    -2.07   0.038    -2.844605   -.0781101
         S_manuf |   .8917814   .5219662     1.71   0.088    -.1312536    1.914816
            div2 |  -.1461299   .1003785    -1.46   0.145    -.3428681    .0506083
            div3 |      .0277   .1143706     0.24   0.809    -.1964622    .2518622
            div4 |  -.0841042   .1232377    -0.68   0.495    -.3256457    .1574373
            div5 |   -.126117   .1299113    -0.97   0.332    -.3807384    .1285044
            div6 |  -.1713554   .1387151    -1.24   0.217    -.4432321    .1005213
            div7 |  -.1440147   .1525284    -0.94   0.345    -.4429649    .1549355
            div8 |  -.3406411   .1587186    -2.15   0.032    -.6517237   -.0295584
            div9 |  -.2815889   .1695661    -1.66   0.097    -.6139324    .0507546
elevat_range_msa |  -.0311476   .0489088    -0.64   0.524    -.1270071     .064712
  ruggedness_msa |   6.168454   3.045415     2.03   0.043      .199551    12.13736
      heating_dd |  -.0130816   .0039166    -3.34   0.001     -.020758   -.0054052
      cooling_dd |   -.015671   .0087907    -1.78   0.075    -.0329005    .0015585
          sprawl |   .0009868    .002687     0.37   0.713    -.0042795    .0062531
           _cons |   12.27195   2.675076     4.59   0.000     7.028895      17.515
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                  
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop S_somecoll
> ege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_ran
> ge_msa ruggedness_msa heating_dd cooling_dd sprawl if year == "_2003",  quantile(10
>  25 50 75 90) vce(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30 done
Quantile = 0.25: .........10.........20.........30 done
Quantile = 0.50: .........10.........20.........30 done
Quantile = 0.75: .........10.........20.........30 done
Quantile = 0.90: .........10.........20.........30 done

IV quantile regression                                Number of obs =      228
Estimator: Inverse quantile regression                Wald chi2(95) = 17128.85
                                                      Prob > chi2   =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.072538    .224791     4.77   0.000     .6319557     1.51312
           l_pop |   .2993676   .1417991     2.11   0.035     .0214464    .5772887
   S_somecollege |   1.110133   1.083423     1.02   0.306    -1.013338    3.233603
   l_mean_income |  -.9413509    .727902    -1.29   0.196    -2.368012    .4853107
          S_poor |  -1.687773   2.640671    -0.64   0.523    -6.863393    3.487847
         S_manuf |   .5575203   .9143614     0.61   0.542    -1.234595    2.349636
            div2 |   .1260281   .6068755     0.21   0.835    -1.063426    1.315482
            div3 |   .2637209   .6372006     0.41   0.679    -.9851694    1.512611
            div4 |  -.0523393   .7333779    -0.07   0.943    -1.489734    1.385055
            div5 |    .185044   .7141944     0.26   0.796    -1.214751    1.584839
            div6 |   .1203718   .7626891     0.16   0.875    -1.374471    1.615215
            div7 |   .1291746   .8272888     0.16   0.876    -1.492282    1.750631
            div8 |  -.3511351   .8722434    -0.40   0.687    -2.060701    1.358431
            div9 |   .1226896   1.235553     0.10   0.921    -2.298949    2.544328
elevat_range_msa |  -.1247582    .216264    -0.58   0.564     -.548628    .2991115
  ruggedness_msa |   13.86475   9.971661     1.39   0.164     -5.67935    33.40884
      heating_dd |   -.008086   .0143609    -0.56   0.573    -.0362328    .0200608
      cooling_dd |  -.0072296   .0260651    -0.28   0.781    -.0583164    .0438571
          sprawl |   .0002979   .0121544     0.02   0.980    -.0235243    .0241201
           _cons |   13.45153    6.35266     2.12   0.034     1.000546    25.90252
-----------------+----------------------------------------------------------------
q25              |
            l_ln |   1.131788   .2523479     4.49   0.000     .6371948     1.62638
           l_pop |   .1867289   .1896195     0.98   0.325    -.1849184    .5583762
   S_somecollege |    1.12856   .8077013     1.40   0.162    -.4545055    2.711626
   l_mean_income |  -.9775218   .5858607    -1.67   0.095    -2.125788    .1707441
          S_poor |  -1.140075   1.918107    -0.59   0.552    -4.899496    2.619346
         S_manuf |   .9696863   .9333513     1.04   0.299    -.8596487    2.799021
            div2 |  -.0355617   .3136133    -0.11   0.910    -.6502324    .5791091
            div3 |   .0851341   .3252703     0.26   0.794     -.552384    .7226521
            div4 |  -.0777018   .2922504    -0.27   0.790     -.650502    .4950984
            div5 |  -.1195694   .3153564    -0.38   0.705    -.7376565    .4985178
            div6 |  -.1605178    .339678    -0.47   0.637    -.8262745     .505239
            div7 |  -.1767392   .4112277    -0.43   0.667    -.9827307    .6292522
            div8 |  -.6726037   .4371091    -1.54   0.124    -1.529322    .1841143
            div9 |  -.2769612   .4478388    -0.62   0.536    -1.154709    .6007867
elevat_range_msa |  -.0101287   .0825762    -0.12   0.902    -.1719751    .1517177
  ruggedness_msa |   9.551833   4.797276     1.99   0.046      .149344    18.95432
      heating_dd |  -.0173018   .0072778    -2.38   0.017    -.0315661   -.0030375
      cooling_dd |  -.0202532   .0186186    -1.09   0.277     -.056745    .0162386
          sprawl |  -.0011694   .0042806    -0.27   0.785    -.0095592    .0072203
           _cons |   15.79112   5.527892     2.86   0.004     4.956651    26.62559
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .8400004   .1542669     5.45   0.000     .5376427    1.142358
           l_pop |   .3532569    .134409     2.63   0.009     .0898201    .6166937
   S_somecollege |   .8336168   .5680359     1.47   0.142    -.2797131    1.946947
   l_mean_income |  -.3818261   .4980555    -0.77   0.443    -1.357997    .5943448
          S_poor |  -1.918058   1.300832    -1.47   0.140    -4.467642    .6315261
         S_manuf |  -.0716372   .6930615    -0.10   0.918    -1.430013    1.286738
            div2 |  -.2452886   .5566113    -0.44   0.659    -1.336227    .8456496
            div3 |   .0031025   .5787282     0.01   0.996    -1.131184    1.137389
            div4 |  -.1490955    .573405    -0.26   0.795    -1.272949    .9747575
            div5 |  -.2019722   .5910542    -0.34   0.733    -1.360417    .9564728
            div6 |  -.1725989   .5878927    -0.29   0.769    -1.324847    .9796495
            div7 |  -.1668011    .590544    -0.28   0.778    -1.324246     .990644
            div8 |  -.3885735     .63277    -0.61   0.539     -1.62878    .8516329
            div9 |  -.3678154   .6611478    -0.56   0.578    -1.663641    .9280105
elevat_range_msa |   -.052005   .1004012    -0.52   0.604    -.2487878    .1447778
  ruggedness_msa |   6.580774   5.343814     1.23   0.218    -3.892908    17.05446
      heating_dd |  -.0176655   .0067339    -2.62   0.009    -.0308638   -.0044672
      cooling_dd |  -.0265654   .0138503    -1.92   0.055    -.0537116    .0005807
          sprawl |   .0022711   .0048703     0.47   0.641    -.0072744    .0118167
           _cons |    10.2798   4.774957     2.15   0.031     .9210516    19.63854
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .6384858   .1629128     3.92   0.000     .3191826     .957789
           l_pop |   .5419205   .1435874     3.77   0.000     .2604943    .8233467
   S_somecollege |   .6154801   .5667147     1.09   0.277    -.4952602     1.72622
   l_mean_income |  -.5774631    .511784    -1.13   0.259    -1.580541    .4256151
          S_poor |  -1.725844   1.302158    -1.33   0.185    -4.278026    .8263391
         S_manuf |  -.3586782   .9902182    -0.36   0.717     -2.29947    1.582114
            div2 |  -.2906728   .1697416    -1.71   0.087    -.6233603    .0420147
            div3 |  -.0326998   .1720487    -0.19   0.849     -.369909    .3045095
            div4 |   -.142279   .2171092    -0.66   0.512    -.5678052    .2832473
            div5 |  -.1153275   .2448742    -0.47   0.638     -.595272    .3646171
            div6 |  -.0839013   .2296144    -0.37   0.715    -.5339374    .3661347
            div7 |  -.1274486   .2337913    -0.55   0.586     -.585671    .3307739
            div8 |  -.2250566   .3057019    -0.74   0.462    -.8242212    .3741081
            div9 |  -.2275086   .2852464    -0.80   0.425    -.7865812     .331564
elevat_range_msa |  -.0706948   .1113208    -0.64   0.525    -.2888797      .14749
  ruggedness_msa |   3.942413   5.576572     0.71   0.480    -6.987467    14.87229
      heating_dd |  -.0093938   .0076336    -1.23   0.218    -.0243554    .0055678
      cooling_dd |  -.0185163   .0160444    -1.15   0.248    -.0499628    .0129303
          sprawl |   .0024951   .0048373     0.52   0.606    -.0069858     .011976
           _cons |   11.06077   4.532374     2.44   0.015     2.177478    19.94406
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .7372836   .1067246     6.91   0.000     .5281073      .94646
           l_pop |   .4018473   .0835261     4.81   0.000     .2381392    .5655554
   S_somecollege |    .803475   .2615208     3.07   0.002     .2909036    1.316046
   l_mean_income |  -.1757545   .2549781    -0.69   0.491    -.6755024    .3239933
          S_poor |  -2.321876    .657637    -3.53   0.000    -3.610821   -1.032932
         S_manuf |    .314221   .5086345     0.62   0.537    -.6826844    1.311126
            div2 |   -.269555   .0873918    -3.08   0.002    -.4408398   -.0982702
            div3 |  -.0658431   .0898432    -0.73   0.464    -.2419324    .1102463
            div4 |  -.1426478   .1241077    -1.15   0.250    -.3858943    .1005988
            div5 |  -.1740207   .0943853    -1.84   0.065    -.3590125     .010971
            div6 |  -.2008308   .1097653    -1.83   0.067    -.4159667    .0143051
            div7 |  -.1054603   .1069251    -0.99   0.324    -.3150296     .104109
            div8 |   -.115922   .1743666    -0.66   0.506    -.4576743    .2258303
            div9 |  -.2950832   .1394136    -2.12   0.034    -.5683288   -.0218376
elevat_range_msa |  -.0744876   .0488395    -1.53   0.127    -.1702112    .0212359
  ruggedness_msa |   4.280503    2.10738     2.03   0.042     .1501138    8.410892
      heating_dd |  -.0119408   .0041912    -2.85   0.004    -.0201554   -.0037262
      cooling_dd |  -.0160423   .0103474    -1.55   0.121    -.0363229    .0042382
          sprawl |    .000438   .0023734     0.18   0.854    -.0042138    .0050898
           _cons |   8.248791   2.273224     3.63   0.000     3.793354    12.70423
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5
            div6 div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd
            cooling_dd sprawl l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            5.573                 2.396
Constant effect |            2.079                 2.400
Dominance       |            0.000                 2.432
Exogeneity      |            1.247                 2.515
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            5.573                 2.174
Constant effect |            2.079                 2.108
Dominance       |            0.000                 2.216
Exogeneity      |            1.247                 2.348
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
.                 
. * Model 5 * 
. 
.                 ivregress liml l_vmt l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 
> l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
>  div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd sprawl (l_ln 
> = l_rail1898 l_hwy1947) if year == "_2003", robust 

Instrumental variables LIML regression            Number of obs   =        228
                                                  Wald chi2(26)   =    6236.55
                                                  Prob > chi2     =     0.0000
                                                  R-squared       =     0.9564
                                                  Root MSE        =     .26707

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
            l_ln |   .9672581   .1472585     6.57   0.000     .6786368    1.255879
           l_pop |   .1173743   .1886499     0.62   0.534    -.2523727    .4871213
         l_pop80 |   .8828435   .4907168     1.80   0.072    -.0789437    1.844631
         l_pop70 |  -.1722967   .5757381    -0.30   0.765    -1.300723    .9561292
         l_pop60 |  -.6157573   .4215833    -1.46   0.144    -1.442045    .2105307
         l_pop50 |   .0103154   .2454949     0.04   0.966    -.4708457    .4914766
         l_pop40 |   .0417089   .2633938     0.16   0.874    -.4745335    .5579512
         l_pop30 |   .0595162   .2498483     0.24   0.812    -.4301775    .5492098
         l_pop20 |   .0153688   .1268345     0.12   0.904    -.2332223    .2639599
   S_somecollege |   .7194102   .3658649     1.97   0.049     .0023282    1.436492
   l_mean_income |    -.62401   .3168861    -1.97   0.049    -1.245095   -.0029248
          S_poor |  -.9611691   .7440869    -1.29   0.196    -2.419553    .4972144
         S_manuf |   .8908962   .5131374     1.74   0.083    -.1148345    1.896627
            div2 |  -.1569872   .1008926    -1.56   0.120    -.3547331    .0407586
            div3 |  -.0004958   .1170454    -0.00   0.997    -.2299005    .2289089
            div4 |  -.0737767   .1209079    -0.61   0.542    -.3107519    .1631984
            div5 |  -.1706845   .1309558    -1.30   0.192    -.4273532    .0859842
            div6 |  -.2087752   .1392332    -1.50   0.134    -.4816673    .0641169
            div7 |  -.1649149   .1543327    -1.07   0.285    -.4674014    .1375717
            div8 |  -.4123736    .200331    -2.06   0.040    -.8050151   -.0197321
            div9 |  -.3199722    .208916    -1.53   0.126    -.7294401    .0894958
elevat_range_msa |  -.0302601   .0488388    -0.62   0.536    -.1259824    .0654623
  ruggedness_msa |   4.302038   3.261091     1.32   0.187    -2.089582    10.69366
      heating_dd |  -.0148555   .0041291    -3.60   0.000    -.0229485   -.0067625
      cooling_dd |  -.0234122   .0094586    -2.48   0.013    -.0419507   -.0048737
          sprawl |   .0000809   .0028467     0.03   0.977    -.0054985    .0056603
           _cons |   11.74726   3.038519     3.87   0.000     5.791868    17.70264
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 ivqregress iqr l_vmt (l_ln = l_rail1898 l_hwy1947) l_pop l_pop80 l_
> pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20 S_somecollege l_mean_income S_poor S_
> manuf div2 div3 div4 div5 div6 div7 div8 div9 elevat_range_msa ruggedness_msa heati
> ng_dd cooling_dd sprawl if year == "_2003",  quantile(10 25 50 75 90) ngrid(100) vc
> e(robust) 

Initial grid:
Quantile = 0.10: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.25: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.50: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.75: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.90: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done

Adaptive grid:
Quantile = 0.10: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.25: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.50: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.75: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done
Quantile = 0.90: .........10.........20.........30.........40.........50
                 .........60.........70.........80.........90.........100 done

IV quantile regression                               Number of obs  =      228
Estimator: Inverse quantile regression               Wald chi2(130) = 14520.80
                                                     Prob > chi2    =   0.0000

----------------------------------------------------------------------------------
                 |               Robust
           l_vmt | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
q10              |
            l_ln |   1.311251   .2392606     5.48   0.000      .842309    1.780193
           l_pop |  -.1527849   .2864463    -0.53   0.594    -.7142093    .4086395
         l_pop80 |   1.226307   .6831462     1.80   0.073    -.1126352    2.565249
         l_pop70 |   .4704709   .9719669     0.48   0.628    -1.434549    2.375491
         l_pop60 |   -1.85879   .8725396    -2.13   0.033    -3.568936   -.1486438
         l_pop50 |   .4190708   1.006752     0.42   0.677    -1.554127    2.392269
         l_pop40 |   .3404842   .4192252     0.81   0.417    -.4811822    1.162151
         l_pop30 |  -.1800596   .4043369    -0.45   0.656    -.9725455    .6124262
         l_pop20 |  -.1663021   .1854629    -0.90   0.370    -.5298026    .1971985
   S_somecollege |   .5261075   1.636779     0.32   0.748    -2.681921    3.734135
   l_mean_income |  -.6566572   .8032503    -0.82   0.414    -2.230999    .9176844
          S_poor |   -.409985   2.511332    -0.16   0.870    -5.332105    4.512135
         S_manuf |   1.444605   .8051754     1.79   0.073    -.1335102    3.022719
            div2 |   .0786439    .551897     0.14   0.887    -1.003054    1.160342
            div3 |   .0599829   .6086902     0.10   0.922    -1.133028    1.252994
            div4 |   .0873271   .5825949     0.15   0.881    -1.054538    1.229192
            div5 |  -.1425778   .7362911    -0.19   0.846    -1.585682    1.300526
            div6 |  -.0902206    .715493    -0.13   0.900    -1.492561     1.31212
            div7 |  -.1675165   .7264521    -0.23   0.818    -1.591336    1.256303
            div8 |  -.5209195    .684811    -0.76   0.447    -1.863124    .8212855
            div9 |  -.1289522    .988486    -0.13   0.896    -2.066349    1.808445
elevat_range_msa |  -.1592058   .1552546    -1.03   0.305    -.4634992    .1450876
  ruggedness_msa |   7.191574    11.3427     0.63   0.526    -15.03971    29.42286
      heating_dd |  -.0187673   .0116261    -1.61   0.106     -.041554    .0040194
      cooling_dd |  -.0286625   .0174566    -1.64   0.101    -.0628768    .0055518
          sprawl |   .0023726   .0071379     0.33   0.740    -.0116173    .0163625
           _cons |   12.31327   6.546836     1.88   0.060    -.5182971    25.14483
-----------------+----------------------------------------------------------------
q25              |
            l_ln |    1.00472   .3402298     2.95   0.003     .3378818    1.671558
           l_pop |  -.1894385   .5251731    -0.36   0.718    -1.218759    .8398818
         l_pop80 |   1.517197   .9076647     1.67   0.095     -.261793    3.296187
         l_pop70 |   -.222787   1.523475    -0.15   0.884    -3.208744     2.76317
         l_pop60 |  -1.081514   1.698878    -0.64   0.524    -4.411252    2.248225
         l_pop50 |  -.0173337   .8017724    -0.02   0.983    -1.588779    1.554111
         l_pop40 |   .2798131   .7314212     0.38   0.702    -1.153746    1.713372
         l_pop30 |  -.0137458   .4998399    -0.03   0.978    -.9934141    .9659225
         l_pop20 |   .0059745   .3872783     0.02   0.988    -.7530771    .7650261
   S_somecollege |   .8333813   1.299024     0.64   0.521    -1.712659    3.379421
   l_mean_income |  -.5812438   1.188024    -0.49   0.625    -2.909727     1.74724
          S_poor |  -1.158515   2.484833    -0.47   0.641    -6.028698    3.711668
         S_manuf |   .6198547   .8091149     0.77   0.444    -.9659814    2.205691
            div2 |   .0526549   .4063162     0.13   0.897    -.7437102      .84902
            div3 |   .1423665   .4795562     0.30   0.767    -.7975464     1.08228
            div4 |   .1125302   .4361434     0.26   0.796    -.7422952    .9673556
            div5 |  -.1630263   .4759722    -0.34   0.732    -1.095915     .769862
            div6 |   -.079368    .479254    -0.17   0.868    -1.018689    .8599526
            div7 |   -.127258   .7048045    -0.18   0.857    -1.508649    1.254133
            div8 |  -.5282909   .4852127    -1.09   0.276     -1.47929    .4227086
            div9 |  -.2670098   .7054502    -0.38   0.705    -1.649667    1.115647
elevat_range_msa |   .0320357   .2576998     0.12   0.901    -.4730466     .537118
  ruggedness_msa |   .7678833   21.58738     0.04   0.972    -41.54261    43.07838
      heating_dd |  -.0235253   .0123286    -1.91   0.056     -.047689    .0006383
      cooling_dd |  -.0358364   .0256141    -1.40   0.162     -.086039    .0143663
          sprawl |   .0017572   .0088629     0.20   0.843    -.0156138    .0191283
           _cons |   11.96383   10.88491     1.10   0.272    -9.370203    33.29786
-----------------+----------------------------------------------------------------
q50              |
            l_ln |   .9383687   .1518446     6.18   0.000     .6407587    1.235979
           l_pop |   .1462088   .2682741     0.54   0.586    -.3795988    .6720163
         l_pop80 |    .433209   .5752178     0.75   0.451    -.6941971    1.560615
         l_pop70 |   .3566696   .5661245     0.63   0.529    -.7529141    1.466253
         l_pop60 |  -.7555275   .4951217    -1.53   0.127    -1.725948    .2148933
         l_pop50 |   -.184811   .3189866    -0.58   0.562    -.8100132    .4403912
         l_pop40 |   .3089121   .2520584     1.23   0.220    -.1851132    .8029374
         l_pop30 |   .1328223   .2026872     0.66   0.512    -.2644374    .5300819
         l_pop20 |  -.1130146   .0860483    -1.31   0.189    -.2816662    .0556371
   S_somecollege |   .6246541   .4690258     1.33   0.183    -.2946197    1.543928
   l_mean_income |  -.5391794   .3684207    -1.46   0.143    -1.261271    .1829118
          S_poor |  -1.251752   .9913958    -1.26   0.207    -3.194852    .6913485
         S_manuf |   .1741141   .6501293     0.27   0.789    -1.100116    1.448344
            div2 |  -.2812984   .1370977    -2.05   0.040    -.5500049   -.0125919
            div3 |  -.0518618   .1360738    -0.38   0.703    -.3185615    .2148379
            div4 |  -.2428732    .166299    -1.46   0.144    -.5688132    .0830668
            div5 |  -.3354236   .1807263    -1.86   0.063    -.6896407    .0187935
            div6 |  -.3027061   .1899007    -1.59   0.111    -.6749047    .0694925
            div7 |  -.3482229   .2016851    -1.73   0.084    -.7435185    .0470727
            div8 |  -.4772441   .3407118    -1.40   0.161    -1.145027    .1905387
            div9 |  -.3188295   .3239317    -0.98   0.325     -.953724    .3160651
elevat_range_msa |  -.0392277   .0730242    -0.54   0.591    -.1823525    .1038971
  ruggedness_msa |   3.602121   4.060985     0.89   0.375    -4.357263    11.56151
      heating_dd |  -.0169645   .0057352    -2.96   0.003    -.0282054   -.0057237
      cooling_dd |  -.0235161   .0125407    -1.88   0.061    -.0480953    .0010632
          sprawl |   .0018987    .003757     0.51   0.613    -.0054648    .0092623
           _cons |    11.6791   3.202866     3.65   0.000       5.4016     17.9566
-----------------+----------------------------------------------------------------
q75              |
            l_ln |   .6609132   .1361428     4.85   0.000     .3940783    .9277481
           l_pop |   .4275389   .2261471     1.89   0.059    -.0157012    .8707791
         l_pop80 |   .0438719   .5777192     0.08   0.939    -1.088437    1.176181
         l_pop70 |   .3559585   .8084486     0.44   0.660    -1.228572    1.940489
         l_pop60 |  -.3721077   .7799607    -0.48   0.633    -1.900803    1.156587
         l_pop50 |    -.11775   .2889786    -0.41   0.684    -.6841377    .4486377
         l_pop40 |   .1714085   .2925991     0.59   0.558    -.4020752    .7448922
         l_pop30 |   .0274576   .2761105     0.10   0.921    -.5137091    .5686242
         l_pop20 |  -.0292481   .1214198    -0.24   0.810    -.2672266    .2087303
   S_somecollege |   .6039937   .4590456     1.32   0.188     -.295719    1.503707
   l_mean_income |  -.5368286   .3640192    -1.47   0.140    -1.250293     .176636
          S_poor |  -1.592633   .9677694    -1.65   0.100    -3.489427    .3041599
         S_manuf |  -.1321525   .5605888    -0.24   0.814    -1.230886    .9665812
            div2 |  -.2658325   .2037275    -1.30   0.192    -.6651311    .1334662
            div3 |   -.026981   .2027635    -0.13   0.894      -.42439    .3704281
            div4 |  -.1600886   .2138023    -0.75   0.454    -.5791335    .2589563
            div5 |  -.1070765   .1940844    -0.55   0.581    -.4874749     .273322
            div6 |  -.0974909   .2184666    -0.45   0.655    -.5256776    .3306958
            div7 |  -.1224049   .2303071    -0.53   0.595    -.5737984    .3289886
            div8 |  -.1207695   .3808422    -0.32   0.751    -.8672065    .6256676
            div9 |  -.1863799   .3395208    -0.55   0.583    -.8518283    .4790686
elevat_range_msa |  -.0828269   .0897736    -0.92   0.356      -.25878    .0931263
  ruggedness_msa |   4.495111   4.220397     1.07   0.287    -3.776716    12.76694
      heating_dd |  -.0114664    .006932    -1.65   0.098    -.0250528      .00212
      cooling_dd |  -.0202171   .0209237    -0.97   0.334    -.0612268    .0207925
          sprawl |   .0015357   .0037377     0.41   0.681      -.00579    .0088615
           _cons |   10.88756   3.477291     3.13   0.002     4.072196    17.70293
-----------------+----------------------------------------------------------------
q90              |
            l_ln |   .8957993     .21381     4.19   0.000     .4767393    1.314859
           l_pop |   .4091004   .3568961     1.15   0.252    -.2904031    1.108604
         l_pop80 |  -.3290643   .8201407    -0.40   0.688     -1.93651    1.278382
         l_pop70 |    .252274    1.03878     0.24   0.808    -1.783698    2.288246
         l_pop60 |  -.1403436   .9817652    -0.14   0.886    -2.064568    1.783881
         l_pop50 |   .3094727    .776194     0.40   0.690     -1.21184    1.830785
         l_pop40 |   -.420432   .7311372    -0.58   0.565    -1.853435    1.012571
         l_pop30 |   .4583466   .4887343     0.94   0.348    -.4995551    1.416248
         l_pop20 |  -.3352194    .283205    -1.18   0.237     -.890291    .2198522
   S_somecollege |   .1994717   .7511894     0.27   0.791    -1.272832    1.671776
   l_mean_income |   -.070329   .3538693    -0.20   0.842       -.7639    .6232421
          S_poor |  -1.731147   1.316935    -1.31   0.189    -4.312292    .8499983
         S_manuf |   .2531602   .8807979     0.29   0.774    -1.473172    1.979492
            div2 |  -.3665691   .0979747    -3.74   0.000    -.5585959   -.1745423
            div3 |  -.2163032   .1423506    -1.52   0.129    -.4953051    .0626988
            div4 |  -.1825242   .1442174    -1.27   0.206    -.4651852    .1001367
            div5 |  -.1866894   .2020593    -0.92   0.356    -.5827183    .2093395
            div6 |  -.2498041   .1956371    -1.28   0.202    -.6332458    .1336375
            div7 |  -.1741357    .237218    -0.73   0.463    -.6390745    .2908031
            div8 |  -.2819609   .3129585    -0.90   0.368    -.8953483    .3314265
            div9 |  -.3872263   .4051408    -0.96   0.339    -1.181288     .406835
elevat_range_msa |  -.0194964     .09484    -0.21   0.837    -.2053793    .1663865
  ruggedness_msa |   1.149018   6.960518     0.17   0.869    -12.49335    14.79138
      heating_dd |  -.0064031   .0087349    -0.73   0.464    -.0235232    .0107171
      cooling_dd |  -.0158072   .0195461    -0.81   0.419    -.0541169    .0225024
          sprawl |   .0004708   .0043857     0.11   0.915    -.0081251    .0090666
           _cons |    8.45627   3.503238     2.41   0.016     1.590049    15.32249
----------------------------------------------------------------------------------
Endogenous: l_ln
Exogenous:  l_pop l_pop80 l_pop70 l_pop60 l_pop50 l_pop40 l_pop30 l_pop20
            S_somecollege l_mean_income S_poor S_manuf div2 div3 div4 div5 div6
            div7 div8 div9 elevat_range_msa ruggedness_msa heating_dd cooling_dd
            sprawl l_rail1898 l_hwy1947

.                 
.                 estat endogeffects, rseed(123456789) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    95% critical value
----------------+---------------------------------------
No effect       |            6.793                 2.614
Constant effect |            3.007                 2.746
Dominance       |            0.000                 2.653
Exogeneity      |            2.322                 2.681
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 estat endogeffects, rseed(123456789) level(90) 

Tests for endogenous effects          Replications = 100
--------------------------------------------------------
Null hypothesis |     KS statistic    90% critical value
----------------+---------------------------------------
No effect       |            6.793                 2.448
Constant effect |            3.007                 2.443
Dominance       |            0.000                 2.395
Exogeneity      |            2.322                 2.348
--------------------------------------------------------
Note: If the KS statistic < critical value, there is
      insufficient evidence to reject the null
      hypothesis. (KS = Kolmogorov–Smirnov)

.                 
.                 
. log off                 
      name:  <unnamed>
       log:  /Users/abhram/Library/CloudStorage/Dropbox/Research/trA/replication/tabl
> e_1.log
  log type:  text
 paused on:  10 May 2023, 18:49:02
-------------------------------------------------------------------------------------
